
@misc{touvron_llama_2023,
	title = {{LLaMA}: {Open} and {Efficient} {Foundation} {Language} {Models}},
	shorttitle = {{LLaMA}},
	abstract = {We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B. We release all our models to the research community.},
	language = {en},
	urldate = {2024-01-12},
	journal = {arXiv.org},
	author = {Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timothée and Rozière, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
	month = feb,
	year = {2023},
	file = {Full Text PDF:C\:\\Users\\320253692\\Zotero\\storage\\B5RJ46G2\\Touvron et al. - 2023 - LLaMA Open and Efficient Foundation Language Mode.pdf:application/pdf},
}

@misc{devlin_bert_2018,
	title = {{BERT}: {Pre}-training of {Deep} {Bidirectional} {Transformers} for {Language} {Understanding}},
	shorttitle = {{BERT}},
	abstract = {We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5\% (7.7\% point absolute improvement), MultiNLI accuracy to 86.7\% (4.6\% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).},
	language = {en},
	urldate = {2024-01-12},
	journal = {arXiv.org},
	author = {Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
	month = oct,
	year = {2018},
	file = {Full Text PDF:C\:\\Users\\320253692\\Zotero\\storage\\2WBQUUZS\\Devlin et al. - 2018 - BERT Pre-training of Deep Bidirectional Transform.pdf:application/pdf},
}

@misc{hamamci_ct2rep_2024,
	title = {{CT2Rep}: {Automated} {Radiology} {Report} {Generation} for {3D} {Medical} {Imaging}},
	shorttitle = {{CT2Rep}},
	abstract = {Medical imaging plays a crucial role in diagnosis, with radiology reports serving as vital documentation. Automating report generation has emerged as a critical need to alleviate the workload of radiologists. While machine learning has facilitated report generation for 2D medical imaging, extending this to 3D has been unexplored due to computational complexity and data scarcity. We introduce the first method to generate radiology reports for 3D medical imaging, specifically targeting chest CT volumes. Given the absence of comparable methods, we establish a baseline using an advanced 3D vision encoder in medical imaging to demonstrate our method's effectiveness, which leverages a novel auto-regressive causal transformer. Furthermore, recognizing the benefits of leveraging information from previous visits, we augment CT2Rep with a cross-attention-based multi-modal fusion module and hierarchical memory, enabling the incorporation of longitudinal multimodal data. Access our code at: https://github.com/ibrahimethemhamamci/CT2Rep},
	urldate = {2024-03-15},
	publisher = {arXiv},
	author = {Hamamci, Ibrahim Ethem and Er, Sezgin and Menze, Bjoern},
	month = mar,
	year = {2024},
	note = {arXiv:2403.06801 [cs, eess]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing},
	file = {arXiv.org Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\XIDX2FY4\\2403.html:text/html;Full Text PDF:C\:\\Users\\320253692\\Zotero\\storage\\2ZNX3BLI\\Hamamci et al. - 2024 - CT2Rep Automated Radiology Report Generation for .pdf:application/pdf},
}

@misc{hamamci_generatect_2023,
	title = {{GenerateCT}: {Text}-{Conditional} {Generation} of {3D} {Chest} {CT} {Volumes}},
	shorttitle = {{GenerateCT}},
	abstract = {In this paper, we introduce GenerateCT, a novel approach for generating CT volumes conditioned on free-form medical text prompts. GenerateCT includes a text encoder and three key components: a novel causal vision transformer for encoding CT volumes, a text-image transformer for aligning CT and text tokens, and a text-conditional super-resolution diffusion model. GenerateCT can produce realistic, high-resolution, and high-fidelity 3D chest CT volumes, validated by low FID and FVD scores. To explore GenerateCT's clinical applications, we evaluated its utility in a multi-abnormality classification task. First, we established a baseline by training a multi-abnormality classifier on our real dataset. To further assess the model's generalization to external datasets and its performance with unseen prompts in a zero-shot scenario, we employed an external dataset to train the classifier, setting an additional benchmark. We conducted two experiments in which we doubled the training datasets by synthesizing an equal number of volumes for each set using GenerateCT. The first experiment demonstrated an 11\% improvement in the AP score when training the classifier jointly on real and generated volumes. The second experiment showed a 7\% improvement when training on both real and generated volumes based on unseen prompts. Moreover, GenerateCT enables the scaling of synthetic training datasets to arbitrary sizes. As an example, we generated 100,000 CT volumes, fivefold the number in our real dataset, and trained the classifier exclusively on these synthetic volumes. Impressively, this classifier surpassed the performance of the one trained on all available real data by a margin of 8\%. Lastly, domain experts evaluated the generated volumes, confirming a high degree of alignment with the text prompt. Our code and pre-trained models are available at: https://github.com/ibrahimethemhamamci/GenerateCT},
	urldate = {2024-01-19},
	publisher = {arXiv},
	author = {Hamamci, Ibrahim Ethem and Er, Sezgin and Simsar, Enis and Sekuboyina, Anjany and Prabhakar, Chinmay and Tezcan, Alperen and Simsek, Ayse Gulnihan and Esirgun, Sevval Nil and Almas, Furkan and Doğan, Irem and Dasdelen, Muhammed Furkan and Reynaud, Hadrien and Pati, Sarthak and Bluethgen, Christian and Ozdemir, Mehmet Kemal and Menze, Bjoern},
	month = nov,
	year = {2023},
	note = {arXiv:2305.16037 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	file = {arXiv.org Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\EVHUEKJ3\\2305.html:text/html;Full Text PDF:C\:\\Users\\320253692\\Zotero\\storage\\RJ6SM7R9\\Hamamci et al. - 2023 - GenerateCT Text-Conditional Generation of 3D Ches.pdf:application/pdf},
}

@misc{liu_exploring_2021,
	title = {Exploring and {Distilling} {Posterior} and {Prior} {Knowledge} for {Radiology} {Report} {Generation}},
	abstract = {Automatically generating radiology reports can improve current clinical practice in diagnostic radiology. On one hand, it can relieve radiologists from the heavy burden of report writing; On the other hand, it can remind radiologists of abnormalities and avoid the misdiagnosis and missed diagnosis. Yet, this task remains a challenging job for data-driven neural networks, due to the serious visual and textual data biases. To this end, we propose a Posterior-and-Prior Knowledge Exploring-and-Distilling approach (PPKED) to imitate the working patterns of radiologists, who will first examine the abnormal regions and assign the disease topic tags to the abnormal regions, and then rely on the years of prior medical knowledge and prior working experience accumulations to write reports. Thus, the PPKED includes three modules: Posterior Knowledge Explorer (PoKE), Prior Knowledge Explorer (PrKE) and Multi-domain Knowledge Distiller (MKD). In detail, PoKE explores the posterior knowledge, which provides explicit abnormal visual regions to alleviate visual data bias; PrKE explores the prior knowledge from the prior medical knowledge graph (medical knowledge) and prior radiology reports (working experience) to alleviate textual data bias. The explored knowledge is distilled by the MKD to generate the final reports. Evaluated on MIMIC-CXR and IU-Xray datasets, our method is able to outperform previous state-of-the-art models on these two datasets.},
	urldate = {2024-01-19},
	publisher = {arXiv},
	author = {Liu, Fenglin and Wu, Xian and Ge, Shen and Fan, Wei and Zou, Yuexian},
	month = jun,
	year = {2021},
	note = {arXiv:2106.06963 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition},
	annote = {Comment: Accepted by CVPR 2021 (2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR2021))},
	file = {arXiv.org Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\WFC4MZUA\\2106.html:text/html},
}

@misc{hamamci_foundation_2024,
	title = {A foundation model utilizing chest {CT} volumes and radiology reports for supervised-level zero-shot detection of abnormalities},
	abstract = {A major challenge in computational research in 3D medical imaging is the lack of comprehensive datasets. Addressing this issue, our study introduces CT-RATE, the first 3D medical imaging dataset that pairs images with textual reports. CT-RATE consists of 25,692 non-contrast chest CT volumes, expanded to 50,188 through various reconstructions, from 21,304 unique patients, along with corresponding radiology text reports. Leveraging CT-RATE, we developed CT-CLIP, a CT-focused contrastive language-image pre-training framework. As a versatile, self-supervised model, CT-CLIP is designed for broad application and does not require task-specific training. Remarkably, CT-CLIP outperforms state-of-the-art, fully supervised methods in multi-abnormality detection across all key metrics, thus eliminating the need for manual annotation. We also demonstrate its utility in case retrieval, whether using imagery or textual queries, thereby advancing knowledge dissemination. The open-source release of CT-RATE and CT-CLIP marks a significant advancement in medical AI, enhancing 3D imaging analysis and fostering innovation in healthcare.},
	urldate = {2024-06-17},
	publisher = {arXiv},
	author = {Hamamci, Ibrahim Ethem and Er, Sezgin and Almas, Furkan and Simsek, Ayse Gulnihan and Esirgun, Sevval Nil and Dogan, Irem and Dasdelen, Muhammed Furkan and Wittmann, Bastian and Simsar, Enis and Simsar, Mehmet and Erdemir, Emine Bensu and Alanbay, Abdullah and Sekuboyina, Anjany and Lafci, Berkan and Ozdemir, Mehmet K. and Menze, Bjoern},
	month = mar,
	year = {2024},
	note = {arXiv:2403.17834 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	file = {arXiv Fulltext PDF:C\:\\Users\\320253692\\Zotero\\storage\\F3TUX7K4\\Hamamci et al. - 2024 - A foundation model utilizing chest CT volumes and .pdf:application/pdf;arXiv.org Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\MDQ9J5VX\\2403.html:text/html},
}

@article{draelos_machine-learning-based_2021,
	title = {Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes},
	volume = {67},
	issn = {1361-8415},
	abstract = {Machine learning models for radiology benefit from large-scale data sets with high quality labels for abnormalities. We curated and analyzed a chest computed tomography (CT) data set of 36,316 volumes from 19,993 unique patients. This is the largest multiply-annotated volumetric medical imaging data set reported. To annotate this data set, we developed a rule-based method for automatically extracting abnormality labels from free-text radiology reports with an average F-score of 0.976 (min 0.941, max 1.0). We also developed a model for multi-organ, multi-disease classification of chest CT volumes that uses a deep convolutional neural network (CNN). This model reached a classification performance of AUROC {\textgreater}0.90 for 18 abnormalities, with an average AUROC of 0.773 for all 83 abnormalities, demonstrating the feasibility of learning from unfiltered whole volume CT data. We show that training on more labels improves performance significantly: for a subset of 9 labels – nodule, opacity, atelectasis, pleural effusion, consolidation, mass, pericardial effusion, cardiomegaly, and pneumothorax – the model's average AUROC increased by 10\% when the number of training labels was increased from 9 to all 83. All code for volume preprocessing, automated label extraction, and the volume abnormality prediction model is publicly available. The 36,316 CT volumes and labels will also be made publicly available pending institutional approval.},
	urldate = {2024-06-17},
	journal = {Medical Image Analysis},
	author = {Draelos, Rachel Lea and Dov, David and Mazurowski, Maciej A. and Lo, Joseph Y. and Henao, Ricardo and Rubin, Geoffrey D. and Carin, Lawrence},
	month = jan,
	year = {2021},
	keywords = {deep learning, chest computed tomography, convolutional neural network, machine learning, multilabel classification},
	pages = {101857},
	file = {Accepted Version:C\:\\Users\\320253692\\Zotero\\storage\\DGTVUH3D\\Draelos et al. - 2021 - Machine-learning-based multiple abnormality predic.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\PHDMFALI\\S1361841520302218.html:text/html},
}

@article{kim_3d_2024,
	title = {{3D} unsupervised anomaly detection through virtual multi-view projection and reconstruction: {Clinical} validation on low-dose chest computed tomography},
	volume = {236},
	issn = {0957-4174},
	shorttitle = {{3D} unsupervised anomaly detection through virtual multi-view projection and reconstruction},
	abstract = {Computer-aided diagnosis for low-dose computed tomography (CT) based on deep learning has recently attracted attention as a first-line automatic testing tool because of its high accuracy and low radiation exposure. We propose a method based on a deep neural network for computer-aided diagnosis called virtual multi-view projection and reconstruction for unsupervised anomaly detection (VMPR-UAD) in low-dose chest CT. Presumably, this is the novel method that only requires data from healthy patients for training to identify three-dimensional (3D) regions containing any anomalies. The method has three key components. Unlike existing computer-aided diagnosis tools that use conventional CT slices as the network input, our method (1) improves the recognition of 3D lung structures by virtually projecting an extracted 3D lung region to obtain two-dimensional (2D) images from diverse views to serve as network inputs, (2) accommodates the input diversity gain for accurate anomaly detection, and (3) achieves 3D anomaly/disease localization through a novel 3D map restoration method using multiple 2D anomaly maps. The proposed method based on unsupervised learning showed a high performance in pneumonia, tuberculosis, and both diseases with patient-level anomaly detection performance of 0.965 area under the curve (AUC) (95\% confidence interval (CI); (0.955, 0.972)), 0.948 AUC (95\% CI; (0.928, 0.966)), and 0.963 AUC (95\% CI; (0.955, 0.970)), respectively. Additionally, our technology visualizes anomalous regions in the 3D perspective. This achieved 93\% accuracy in visualizing the location of lung cancer lesions through external validation. These results highlight the potential of a new AI methodology without utilizing disease data learning; this can secure AI model prediction stability by reducing the false negative rate that occurs in various patterns of diseases.},
	urldate = {2024-06-17},
	journal = {Expert Systems with Applications},
	author = {Kim, Kyungsu and Oh, Seong Je and Lee, Ju Hwan and Chung, Myung Jin},
	month = feb,
	year = {2024},
	keywords = {Deep neural network, Low-dose computed tomography, Unsupervised anomaly detection, Unsupervised anomaly localization, Virtual multi-view projection and reconstruction},
	pages = {121165},
	file = {ScienceDirect Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\TDKUJUIB\\S0957417423016676.html:text/html},
}

@misc{dosovitskiy_image_2021,
	title = {An {Image} is {Worth} 16x16 {Words}: {Transformers} for {Image} {Recognition} at {Scale}},
	shorttitle = {An {Image} is {Worth} 16x16 {Words}},
	abstract = {While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train.},
	urldate = {2024-06-17},
	publisher = {arXiv},
	author = {Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
	month = jun,
	year = {2021},
	note = {arXiv:2010.11929 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Artificial Intelligence},
	annote = {Comment: Fine-tuning code and pre-trained models are available at https://github.com/google-research/vision\_transformer. ICLR camera-ready version with 2 small modifications: 1) Added a discussion of CLS vs GAP classifier in the appendix, 2) Fixed an error in exaFLOPs computation in Figure 5 and Table 6 (relative performance of models is basically not affected)},
	file = {arXiv Fulltext PDF:C\:\\Users\\320253692\\Zotero\\storage\\JC7ZYBN7\\Dosovitskiy et al. - 2021 - An Image is Worth 16x16 Words Transformers for Im.pdf:application/pdf;arXiv.org Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\SKCHMF9R\\2010.html:text/html},
}

@misc{he_deep_2015,
	title = {Deep {Residual} {Learning} for {Image} {Recognition}},
	abstract = {Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57\% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28\% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC \& COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.},
	urldate = {2024-06-17},
	publisher = {arXiv},
	author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
	month = dec,
	year = {2015},
	note = {arXiv:1512.03385 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	annote = {Comment: Tech report},
	file = {arXiv Fulltext PDF:C\:\\Users\\320253692\\Zotero\\storage\\7P4ESSQU\\He et al. - 2015 - Deep Residual Learning for Image Recognition.pdf:application/pdf;arXiv.org Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\Y6KUZPAZ\\1512.html:text/html},
}

@article{radford_language_nodate,
	title = {Language {Models} are {Unsupervised} {Multitask} {Learners}},
	abstract = {Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspeciﬁc datasets. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText. When conditioned on a document plus questions, the answers generated by the language model reach 55 F1 on the CoQA dataset - matching or exceeding the performance of 3 out of 4 baseline systems without using the 127,000+ training examples. The capacity of the language model is essential to the success of zero-shot task transfer and increasing it improves performance in a log-linear fashion across tasks. Our largest model, GPT-2, is a 1.5B parameter Transformer that achieves state of the art results on 7 out of 8 tested language modeling datasets in a zero-shot setting but still underﬁts WebText. Samples from the model reﬂect these improvements and contain coherent paragraphs of text. These ﬁndings suggest a promising path towards building language processing systems which learn to perform tasks from their naturally occurring demonstrations.},
	language = {en},
	author = {Radford, Alec and Wu, Jeffrey and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
	file = {Radford et al. - Language Models are Unsupervised Multitask Learner.pdf:C\:\\Users\\320253692\\Zotero\\storage\\ZC7FKVQB\\Radford et al. - Language Models are Unsupervised Multitask Learner.pdf:application/pdf},
}

@misc{vaswani_attention_2023,
	title = {Attention {Is} {All} {You} {Need}},
	abstract = {The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.},
	urldate = {2024-06-25},
	publisher = {arXiv},
	author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Lukasz and Polosukhin, Illia},
	month = aug,
	year = {2023},
	note = {arXiv:1706.03762 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning},
	annote = {Comment: 15 pages, 5 figures},
	file = {arXiv Fulltext PDF:C\:\\Users\\320253692\\Zotero\\storage\\HG6TZALW\\Vaswani et al. - 2023 - Attention Is All You Need.pdf:application/pdf;arXiv.org Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\BPNG6YK7\\1706.html:text/html},
}

@article{zhao_3d_2021,
	title = {{3D} multi-scale, multi-task, and multi-label deep learning for prediction of lymph node metastasis in {T1} lung adenocarcinoma patients' {CT} images},
	volume = {93},
	issn = {1879-0771},
	abstract = {The diagnosis of preoperative lymph node (LN) metastasis is crucial to evaluate possible therapy options for T1 lung adenocarcinoma patients. Radiologists preoperatively diagnose LN metastasis by evaluating signs related to LN metastasis, like spiculation or lobulation of pulmonary nodules in CT images. However, this type of evaluation is subjective and time-consuming, which may result in poor consistency and low efficiency of diagnoses. In this study, a 3D Multi-scale, Multi-task, and Multi-label classification network (3M-CN) was proposed to predict LN metastasis, as well as evaluate multiple related signs of pulmonary nodules in order to improve the accuracy of LN metastasis prediction. The following key approaches were adapted for this method. First, a multi-scale feature fusion module was proposed to aggregate the features from different levels for which different labels be best modeled at different levels; second, an auxiliary segmentation task was applied to force the model to focus more on the nodule region and less on surrounding unrelated structures; and third, a cross-modal integration module called the refine layer was designed to integrate the related risk factors into the model to further improve its confidence level. The 3M-CN was trained using data from 401 cases and then validated on both internal and external datasets, which consisted of 100 cases and 53 cases, respectively. The proposed 3M-CN model was then compared with existing state-of-the-art methods for prediction of LN metastasis. The proposed model outperformed other methods, achieving the best performance with AUCs of 0.945 and 0.948 in the internal and external test datasets, respectively. The proposed model not only obtain strong generalization, but greatly enhance the interpretability of the deep learning model, increase doctors' confidence in the model results, conform to doctors' diagnostic process, and may also be transferable to the diagnosis of other diseases.},
	language = {eng},
	journal = {Computerized Medical Imaging and Graphics: The Official Journal of the Computerized Medical Imaging Society},
	author = {Zhao, Xingyu and Wang, Xiang and Xia, Wei and Zhang, Rui and Jian, Junming and Zhang, Jiayi and Zhu, Yechen and Tang, Yuguo and Li, Zhen and Liu, Shiyuan and Gao, Xin},
	month = oct,
	year = {2021},
	pmid = {34610501},
	keywords = {Humans, Lung Neoplasms, Tomography, X-Ray Computed, 3D convolutional neural network, Adenocarcinoma of Lung, Deep Learning, Lymph node metastasis prediction, Lymphatic Metastasis, Pulmonary nodule signs},
	pages = {101987},
}

@incollection{denotter_hounsfield_2024,
	address = {Treasure Island (FL)},
	title = {Hounsfield {Unit}},
	copyright = {Copyright © 2024, StatPearls Publishing LLC.},
	abstract = {The Hounsfield unit (HU) is a relative quantitative measurement of radio density used by radiologists in the interpretation of computed tomography (CT) images. The absorption/attenuation coefficient of radiation within a tissue is used during CT reconstruction to produce a grayscale image. The physical density of tissue is proportional to the absorption/attenuation of the X-ray beam. The Hounsfield unit, also referred to as the CT unit, is then calculated based on a linear transformation of the baseline linear attenuation coefficient of the X-ray beam, where distilled water (at standard temperature and pressure) is arbitrarily defined to be zero Hounsfield Units and air defined as -1000 HU. The upper limits can reach up to 1000 for bones, 2000 for dense bones like the cochlea, and more than 3000 for metals like steel or silver. The linear transformation produces a Hounsfield scale that displays as gray tones. More dense tissue, with greater X-ray beam absorption, has positive values and appears bright; less dense tissue, with less X-ray beam absorption, has negative values and appears dark. The Hounsfield unit was named after Sir Godfrey Hounsfield, recipient of the Nobel Prize in Physiology or Medicine in 1979, for his part in the invention of CT, as it had immediate recognition as a revolutionary diagnostic instrument.},
	language = {eng},
	urldate = {2024-06-27},
	booktitle = {{StatPearls}},
	publisher = {StatPearls Publishing},
	author = {DenOtter, Tami D. and Schubert, Johanna},
	year = {2024},
	pmid = {31613501},
	file = {Printable HTML:C\:\\Users\\320253692\\Zotero\\storage\\JY33GM8M\\NBK547721.html:text/html},
}

@article{mai_systematic_2023,
	title = {A systematic review of automated segmentation of {3D} computed‐tomography scans for volumetric body composition analysis},
	volume = {14},
	issn = {2190-5991},
	abstract = {Automated computed tomography (CT) scan segmentation (labelling of pixels according to tissue type) is now possible. This technique is being adapted to achieve three‐dimensional (3D) segmentation of CT scans, opposed to single L3‐slice alone. This systematic review evaluates feasibility and accuracy of automated segmentation of 3D CT scans for volumetric body composition (BC) analysis, as well as current limitations and pitfalls clinicians and researchers should be aware of. OVID Medline, Embase and grey literature databases up to October 2021 were searched. Original studies investigating automated skeletal muscle, visceral and subcutaneous AT segmentation from CT were included. Seven of the 92 studies met inclusion criteria. Variation existed in expertise and numbers of humans performing ground‐truth segmentations used to train algorithms. There was heterogeneity in patient characteristics, pathology and CT phases that segmentation algorithms were developed upon. Reporting of anatomical CT coverage varied, with confusing terminology. Six studies covered volumetric regional slabs rather than the whole body. One study stated the use of whole‐body CT, but it was not clear whether this truly meant head‐to‐fingertip‐to‐toe. Two studies used conventional computer algorithms. The latter five used deep learning (DL), an artificial intelligence technique where algorithms are similarly organized to brain neuronal pathways. Six of seven reported excellent segmentation performance (Dice similarity coefficients {\textgreater} 0.9 per tissue). Internal testing on unseen scans was performed for only four of seven algorithms, whilst only three were tested externally. Trained DL algorithms achieved full CT segmentation in 12 to 75 s versus 25 min for non‐DL techniques. DL enables opportunistic, rapid and automated volumetric BC analysis of CT performed for clinical indications. However, most CT scans do not cover head‐to‐fingertip‐to‐toe; further research must validate using common CT regions to estimate true whole‐body BC, with direct comparison to single lumbar slice. Due to successes of DL, we expect progressive numbers of algorithms to materialize in addition to the seven discussed in this paper. Researchers and clinicians in the field of BC must therefore be aware of pitfalls. High Dice similarity coefficients do not inform the degree to which BC tissues may be under‐ or overestimated and nor does it inform on algorithm precision. Consensus is needed to define accuracy and precision standards for ground‐truth labelling. Creation of a large international, multicentre common CT dataset with BC ground‐truth labels from multiple experts could be a robust solution.},
	number = {5},
	urldate = {2024-07-09},
	journal = {Journal of Cachexia, Sarcopenia and Muscle},
	author = {Mai, Dinh Van Chi and Drami, Ioanna and Pring, Edward T. and Gould, Laura E. and Lung, Phillip and Popuri, Karteek and Chow, Vincent and Beg, Mirza F. and Athanasiou, Thanos and Jenkins, John T.},
	month = aug,
	year = {2023},
	pmid = {37562946},
	pmcid = {PMC10570079},
	pages = {1973--1986},
	file = {PubMed Central Full Text PDF:C\:\\Users\\320253692\\Zotero\\storage\\CJIYMN2J\\Mai et al. - 2023 - A systematic review of automated segmentation of 3.pdf:application/pdf},
}

@article{ilesanmi_reviewing_2024,
	title = {Reviewing {3D} convolutional neural network approaches for medical image segmentation},
	volume = {10},
	issn = {2405-8440},
	abstract = {Background
Convolutional neural networks (CNNs) assume pivotal roles in aiding clinicians in diagnosis and treatment decisions. The rapid evolution of imaging technology has established three-dimensional (3D) CNNs as a formidable framework for delineating organs and anomalies in medical images. The prominence of 3D CNN frameworks is steadily growing within medical image segmentation and classification. Thus, our proposition entails a comprehensive review, encapsulating diverse 3D CNN algorithms for the segmentation of medical image anomalies and organs.
Methods
This study systematically presents an exhaustive review of recent 3D CNN methodologies. Rigorous screening of abstracts and titles were carried out to establish their relevance. Research papers disseminated across academic repositories were meticulously chosen, analyzed, and appraised against specific criteria. Insights into the realm of anomalies and organ segmentation were derived, encompassing details such as network architecture and achieved accuracies.
Results
This paper offers an all-encompassing analysis, unveiling the prevailing trends in 3D CNN segmentation. In-depth elucidations encompass essential insights, constraints, observations, and avenues for future exploration. A discerning examination indicates the preponderance of the encoder-decoder network in segmentation tasks. The encoder-decoder framework affords a coherent methodology for the segmentation of medical images.
Conclusion
The findings of this study are poised to find application in clinical diagnosis and therapeutic interventions. Despite inherent limitations, CNN algorithms showcase commendable accuracy levels, solidifying their potential in medical image segmentation and classification endeavors.},
	number = {6},
	urldate = {2024-07-09},
	journal = {Heliyon},
	author = {Ilesanmi, Ademola E. and Ilesanmi, Taiwo O. and Ajayi, Babatunde O.},
	month = mar,
	year = {2024},
	keywords = {3D convolutional neural network, Medical images, Segmentation of abnormalities and organs},
	pages = {e27398},
	file = {Full Text:C\:\\Users\\320253692\\Zotero\\storage\\NS8TBL5N\\Ilesanmi et al. - 2024 - Reviewing 3D convolutional neural network approach.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\2EVPYFLW\\S2405844024034297.html:text/html},
}

@misc{chen_unit3d_2022,
	title = {{UniT3D}: {A} {Unified} {Transformer} for {3D} {Dense} {Captioning} and {Visual} {Grounding}},
	shorttitle = {{UniT3D}},
	abstract = {Performing 3D dense captioning and visual grounding requires a common and shared understanding of the underlying multimodal relationships. However, despite some previous attempts on connecting these two related tasks with highly task-specific neural modules, it remains understudied how to explicitly depict their shared nature to learn them simultaneously. In this work, we propose UniT3D, a simple yet effective fully unified transformer-based architecture for jointly solving 3D visual grounding and dense captioning. UniT3D enables learning a strong multimodal representation across the two tasks through a supervised joint pre-training scheme with bidirectional and seq-to-seq objectives. With a generic architecture design, UniT3D allows expanding the pre-training scope to more various training sources such as the synthesized data from 2D prior knowledge to benefit 3D vision-language tasks. Extensive experiments and analysis demonstrate that UniT3D obtains significant gains for 3D dense captioning and visual grounding.},
	urldate = {2024-07-12},
	publisher = {arXiv},
	author = {Chen, Dave Zhenyu and Hu, Ronghang and Chen, Xinlei and Nießner, Matthias and Chang, Angel X.},
	month = dec,
	year = {2022},
	note = {arXiv:2212.00836 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	file = {arXiv Fulltext PDF:C\:\\Users\\320253692\\Zotero\\storage\\RMNA2P8H\\Chen et al. - 2022 - UniT3D A Unified Transformer for 3D Dense Caption.pdf:application/pdf;arXiv.org Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\UTNTZEVJ\\2212.html:text/html},
}

@misc{liu_benchmarking_2024,
	title = {Benchmarking and {Boosting} {Radiology} {Report} {Generation} for {3D} {High}-{Resolution} {Medical} {Images}},
	abstract = {Automatic radiology report generation can significantly benefit the labor-intensive process of report writing by radiologists, especially for 3D radiographs like CT scans, which are crucial for broad clinical diagnostics yet underexplored compared to 2D radiographs. Existing methods often handle 3D volumes either slice-wise or with aggressive downsampling due to current GPU memory limitations, which results in a loss of the inherent 3D nature and critical details. To overcome these issues, we introduce a novel framework that efficiently and effectively generates radiology reports for high-resolution (HR) 3D volumes, based on large language models (LLMs). Specifically, our framework utilizes low-resolution (LR) visual tokens as queries to mine information from HR tokens, preserving detailed HR information while reducing computational costs by only processing HR informed LR visual queries. Further benefiting the field, we curate and release BIMCV-RG, a new dataset with 5,328 HR 3D volumes and paired reports, establishing the first benchmarks for report generation from 3D HR medical images. Our method consistently surpasses existing methods on this benchmark across three different settings: normal-resolution, high-resolution inputs, and zero-shot domain transfer, all at an acceptable computational cost, trainable on a single A100-80G.},
	urldate = {2024-07-25},
	publisher = {arXiv},
	author = {Liu, Che and Wan, Zhongwei and Wang, Yuqi and Shen, Hui and Wang, Haozhe and Zheng, Kangyu and Zhang, Mi and Arcucci, Rossella},
	month = jun,
	year = {2024},
	note = {arXiv:2406.07146 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence},
	file = {arXiv Fulltext PDF:C\:\\Users\\320253692\\Zotero\\storage\\EFZ92SVI\\Liu et al. - 2024 - Benchmarking and Boosting Radiology Report Generat.pdf:application/pdf;arXiv.org Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\W48AJ4ZV\\2406.html:text/html},
}

@article{broder_increasing_2006,
	title = {Increasing utilization of computed tomography in the adult emergency department, 2000-2005},
	volume = {13},
	issn = {1070-3004},
	abstract = {This study aims to characterize changes in computed tomography (CT) utilization in the adult emergency department (ED) over a 5-year period. CT scans ordered on adult ED patients from July 2000 to July 2005 were analyzed in five groups: head, cervical spine, chest, abdomen, and miscellaneous. ED patient volume and triage acuity scores were determined. Triage acuity scores are used to determine the severity of a patient's illness or injury and the need for immediate evaluation and treatment. There were 46,553 CT scans performed on 27,625 adult patients in the ED during the study period. During this same period, 194,622 adult patients were evaluated in the ED. From 2000 to 2005, the adult emergency department patient volume increased by 13\% while triage acuity remained stable. During this same period, head CT increased by 51\%, cervical spine CT by 463\%, chest CT by 226\%, abdominal CT by 72\%, and miscellaneous CT by 132\%. Although increases were generally greater for patients over age 40, the increase in those less than 40 years was also substantial. Of the 4,320 individual patients who underwent chest CT, 83 (2\%) had chest CT on three or more separate ED visits. Of 10,960 patients undergoing abdominal CT, 406 (4\%) had abdominal CT on three or more separate ED visits. ED CT utilization has increased at a rate far exceeding the growth in ED patient volume. This presumably reflects the improved utility of CT in diagnosing serious pathology, its increased availability, and a desire on the part of physicians for diagnostic certainty. Whether this increase in utilization results in improved patient outcomes is at present unclear and deserves additional study.},
	language = {eng},
	number = {1},
	journal = {Emergency Radiology},
	author = {Broder, Joshua and Warshauer, David M.},
	month = oct,
	year = {2006},
	pmid = {16900352},
	keywords = {Female, Humans, Male, Middle Aged, Radiography, Thoracic, Tomography, X-Ray Computed, Emergency Service, Hospital, Adult, Adolescent, Cervical Vertebrae, Head, North Carolina, Radiography, Abdominal, Severity of Illness Index},
	pages = {25--30},
}

@misc{dubey_llama_2024,
	title = {The {Llama} 3 {Herd} of {Models}},
	abstract = {Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.},
	urldate = {2024-09-10},
	publisher = {arXiv},
	author = {Dubey, Abhimanyu and Jauhri, Abhinav and Pandey, Abhinav and Kadian, Abhishek and Al-Dahle, Ahmad and Letman, Aiesha and Mathur, Akhil and Schelten, Alan and Yang, Amy and Fan, Angela and Goyal, Anirudh and Hartshorn, Anthony and Yang, Aobo and Mitra, Archi and Sravankumar, Archie and Korenev, Artem and Hinsvark, Arthur and Rao, Arun and Zhang, Aston and Rodriguez, Aurelien and Gregerson, Austen and Spataru, Ava and Roziere, Baptiste and Biron, Bethany and Tang, Binh and Chern, Bobbie and Caucheteux, Charlotte and Nayak, Chaya and Bi, Chloe and Marra, Chris and McConnell, Chris and Keller, Christian and Touret, Christophe and Wu, Chunyang and Wong, Corinne and Ferrer, Cristian Canton and Nikolaidis, Cyrus and Allonsius, Damien and Song, Daniel and Pintz, Danielle and Livshits, Danny and Esiobu, David and Choudhary, Dhruv and Mahajan, Dhruv and Garcia-Olano, Diego and Perino, Diego and Hupkes, Dieuwke and Lakomkin, Egor and AlBadawy, Ehab and Lobanova, Elina and Dinan, Emily and Smith, Eric Michael and Radenovic, Filip and Zhang, Frank and Synnaeve, Gabriel and Lee, Gabrielle and Anderson, Georgia Lewis and Nail, Graeme and Mialon, Gregoire and Pang, Guan and Cucurell, Guillem and Nguyen, Hailey and Korevaar, Hannah and Xu, Hu and Touvron, Hugo and Zarov, Iliyan and Ibarra, Imanol Arrieta and Kloumann, Isabel and Misra, Ishan and Evtimov, Ivan and Copet, Jade and Lee, Jaewon and Geffert, Jan and Vranes, Jana and Park, Jason and Mahadeokar, Jay and Shah, Jeet and van der Linde, Jelmer and Billock, Jennifer and Hong, Jenny and Lee, Jenya and Fu, Jeremy and Chi, Jianfeng and Huang, Jianyu and Liu, Jiawen and Wang, Jie and Yu, Jiecao and Bitton, Joanna and Spisak, Joe and Park, Jongsoo and Rocca, Joseph and Johnstun, Joshua and Saxe, Joshua and Jia, Junteng and Alwala, Kalyan Vasuden and Upasani, Kartikeya and Plawiak, Kate and Li, Ke and Heafield, Kenneth and Stone, Kevin and El-Arini, Khalid and Iyer, Krithika and Malik, Kshitiz and Chiu, Kuenley and Bhalla, Kunal and Rantala-Yeary, Lauren and van der Maaten, Laurens and Chen, Lawrence and Tan, Liang and Jenkins, Liz and Martin, Louis and Madaan, Lovish and Malo, Lubo and Blecher, Lukas and Landzaat, Lukas and de Oliveira, Luke and Muzzi, Madeline and Pasupuleti, Mahesh and Singh, Mannat and Paluri, Manohar and Kardas, Marcin and Oldham, Mathew and Rita, Mathieu and Pavlova, Maya and Kambadur, Melanie and Lewis, Mike and Si, Min and Singh, Mitesh Kumar and Hassan, Mona and Goyal, Naman and Torabi, Narjes and Bashlykov, Nikolay and Bogoychev, Nikolay and Chatterji, Niladri and Duchenne, Olivier and Çelebi, Onur and Alrassy, Patrick and Zhang, Pengchuan and Li, Pengwei and Vasic, Petar and Weng, Peter and Bhargava, Prajjwal and Dubal, Pratik and Krishnan, Praveen and Koura, Punit Singh and Xu, Puxin and He, Qing and Dong, Qingxiao and Srinivasan, Ragavan and Ganapathy, Raj and Calderer, Ramon and Cabral, Ricardo Silveira and Stojnic, Robert and Raileanu, Roberta and Girdhar, Rohit and Patel, Rohit and Sauvestre, Romain and Polidoro, Ronnie and Sumbaly, Roshan and Taylor, Ross and Silva, Ruan and Hou, Rui and Wang, Rui and Hosseini, Saghar and Chennabasappa, Sahana and Singh, Sanjay and Bell, Sean and Kim, Seohyun Sonia and Edunov, Sergey and Nie, Shaoliang and Narang, Sharan and Raparthy, Sharath and Shen, Sheng and Wan, Shengye and Bhosale, Shruti and Zhang, Shun and Vandenhende, Simon and Batra, Soumya and Whitman, Spencer and Sootla, Sten and Collot, Stephane and Gururangan, Suchin and Borodinsky, Sydney and Herman, Tamar and Fowler, Tara and Sheasha, Tarek and Georgiou, Thomas and Scialom, Thomas and Speckbacher, Tobias and Mihaylov, Todor and Xiao, Tong and Karn, Ujjwal and Goswami, Vedanuj and Gupta, Vibhor and Ramanathan, Vignesh and Kerkez, Viktor and Gonguet, Vincent and Do, Virginie and Vogeti, Vish and Petrovic, Vladan and Chu, Weiwei and Xiong, Wenhan and Fu, Wenyin and Meers, Whitney and Martinet, Xavier and Wang, Xiaodong and Tan, Xiaoqing Ellen and Xie, Xinfeng and Jia, Xuchao and Wang, Xuewei and Goldschlag, Yaelle and Gaur, Yashesh and Babaei, Yasmine and Wen, Yi and Song, Yiwen and Zhang, Yuchen and Li, Yue and Mao, Yuning and Coudert, Zacharie Delpierre and Yan, Zheng and Chen, Zhengxing and Papakipos, Zoe and Singh, Aaditya and Grattafiori, Aaron and Jain, Abha and Kelsey, Adam and Shajnfeld, Adam and Gangidi, Adithya and Victoria, Adolfo and Goldstand, Ahuva and Menon, Ajay and Sharma, Ajay and Boesenberg, Alex and Vaughan, Alex and Baevski, Alexei and Feinstein, Allie and Kallet, Amanda and Sangani, Amit and Yunus, Anam and Lupu, Andrei and Alvarado, Andres and Caples, Andrew and Gu, Andrew and Ho, Andrew and Poulton, Andrew and Ryan, Andrew and Ramchandani, Ankit and Franco, Annie and Saraf, Aparajita and Chowdhury, Arkabandhu and Gabriel, Ashley and Bharambe, Ashwin and Eisenman, Assaf and Yazdan, Azadeh and James, Beau and Maurer, Ben and Leonhardi, Benjamin and Huang, Bernie and Loyd, Beth and De Paola, Beto and Paranjape, Bhargavi and Liu, Bing and Wu, Bo and Ni, Boyu and Hancock, Braden and Wasti, Bram and Spence, Brandon and Stojkovic, Brani and Gamido, Brian and Montalvo, Britt and Parker, Carl and Burton, Carly and Mejia, Catalina and Wang, Changhan and Kim, Changkyu and Zhou, Chao and Hu, Chester and Chu, Ching-Hsiang and Cai, Chris and Tindal, Chris and Feichtenhofer, Christoph and Civin, Damon and Beaty, Dana and Kreymer, Daniel and Li, Daniel and Wyatt, Danny and Adkins, David and Xu, David and Testuggine, Davide and David, Delia and Parikh, Devi and Liskovich, Diana and Foss, Didem and Wang, Dingkang and Le, Duc and Holland, Dustin and Dowling, Edward and Jamil, Eissa and Montgomery, Elaine and Presani, Eleonora and Hahn, Emily and Wood, Emily and Brinkman, Erik and Arcaute, Esteban and Dunbar, Evan and Smothers, Evan and Sun, Fei and Kreuk, Felix and Tian, Feng and Ozgenel, Firat and Caggioni, Francesco and Guzmán, Francisco and Kanayet, Frank and Seide, Frank and Florez, Gabriela Medina and Schwarz, Gabriella and Badeer, Gada and Swee, Georgia and Halpern, Gil and Thattai, Govind and Herman, Grant and Sizov, Grigory and Guangyi and Zhang and Lakshminarayanan, Guna and Shojanazeri, Hamid and Zou, Han and Wang, Hannah and Zha, Hanwen and Habeeb, Haroun and Rudolph, Harrison and Suk, Helen and Aspegren, Henry and Goldman, Hunter and Damlaj, Ibrahim and Molybog, Igor and Tufanov, Igor and Veliche, Irina-Elena and Gat, Itai and Weissman, Jake and Geboski, James and Kohli, James and Asher, Japhet and Gaya, Jean-Baptiste and Marcus, Jeff and Tang, Jeff and Chan, Jennifer and Zhen, Jenny and Reizenstein, Jeremy and Teboul, Jeremy and Zhong, Jessica and Jin, Jian and Yang, Jingyi and Cummings, Joe and Carvill, Jon and Shepard, Jon and McPhie, Jonathan and Torres, Jonathan and Ginsburg, Josh and Wang, Junjie and Wu, Kai and U, Kam Hou and Saxena, Karan and Prasad, Karthik and Khandelwal, Kartikay and Zand, Katayoun and Matosich, Kathy and Veeraraghavan, Kaushik and Michelena, Kelly and Li, Keqian and Huang, Kun and Chawla, Kunal and Lakhotia, Kushal and Huang, Kyle and Chen, Lailin and Garg, Lakshya and A, Lavender and Silva, Leandro and Bell, Lee and Zhang, Lei and Guo, Liangpeng and Yu, Licheng and Moshkovich, Liron and Wehrstedt, Luca and Khabsa, Madian and Avalani, Manav and Bhatt, Manish and Tsimpoukelli, Maria and Mankus, Martynas and Hasson, Matan and Lennie, Matthew and Reso, Matthias and Groshev, Maxim and Naumov, Maxim and Lathi, Maya and Keneally, Meghan and Seltzer, Michael L. and Valko, Michal and Restrepo, Michelle and Patel, Mihir and Vyatskov, Mik and Samvelyan, Mikayel and Clark, Mike and Macey, Mike and Wang, Mike and Hermoso, Miquel Jubert and Metanat, Mo and Rastegari, Mohammad and Bansal, Munish and Santhanam, Nandhini and Parks, Natascha and White, Natasha and Bawa, Navyata and Singhal, Nayan and Egebo, Nick and Usunier, Nicolas and Laptev, Nikolay Pavlovich and Dong, Ning and Zhang, Ning and Cheng, Norman and Chernoguz, Oleg and Hart, Olivia and Salpekar, Omkar and Kalinli, Ozlem and Kent, Parkin and Parekh, Parth and Saab, Paul and Balaji, Pavan and Rittner, Pedro and Bontrager, Philip and Roux, Pierre and Dollar, Piotr and Zvyagina, Polina and Ratanchandani, Prashant and Yuvraj, Pritish and Liang, Qian and Alao, Rachad and Rodriguez, Rachel and Ayub, Rafi and Murthy, Raghotham and Nayani, Raghu and Mitra, Rahul and Li, Raymond and Hogan, Rebekkah and Battey, Robin and Wang, Rocky and Maheswari, Rohan and Howes, Russ and Rinott, Ruty and Bondu, Sai Jayesh and Datta, Samyak and Chugh, Sara and Hunt, Sara and Dhillon, Sargun and Sidorov, Sasha and Pan, Satadru and Verma, Saurabh and Yamamoto, Seiji and Ramaswamy, Sharadh and Lindsay, Shaun and Lindsay, Shaun and Feng, Sheng and Lin, Shenghao and Zha, Shengxin Cindy and Shankar, Shiva and Zhang, Shuqiang and Zhang, Shuqiang and Wang, Sinong and Agarwal, Sneha and Sajuyigbe, Soji and Chintala, Soumith and Max, Stephanie and Chen, Stephen and Kehoe, Steve and Satterfield, Steve and Govindaprasad, Sudarshan and Gupta, Sumit and Cho, Sungmin and Virk, Sunny and Subramanian, Suraj and Choudhury, Sy and Goldman, Sydney and Remez, Tal and Glaser, Tamar and Best, Tamara and Kohler, Thilo and Robinson, Thomas and Li, Tianhe and Zhang, Tianjun and Matthews, Tim and Chou, Timothy and Shaked, Tzook and Vontimitta, Varun and Ajayi, Victoria and Montanez, Victoria and Mohan, Vijai and Kumar, Vinay Satish and Mangla, Vishal and Albiero, Vítor and Ionescu, Vlad and Poenaru, Vlad and Mihailescu, Vlad Tiberiu and Ivanov, Vladimir and Li, Wei and Wang, Wenchen and Jiang, Wenwen and Bouaziz, Wes and Constable, Will and Tang, Xiaocheng and Wang, Xiaofang and Wu, Xiaojian and Wang, Xiaolan and Xia, Xide and Wu, Xilun and Gao, Xinbo and Chen, Yanjun and Hu, Ye and Jia, Ye and Qi, Ye and Li, Yenda and Zhang, Yilin and Zhang, Ying and Adi, Yossi and Nam, Youngjin and Yu and Wang and Hao, Yuchen and Qian, Yundi and He, Yuzi and Rait, Zach and DeVito, Zachary and Rosnbrick, Zef and Wen, Zhaoduo and Yang, Zhenyu and Zhao, Zhiwei},
	month = aug,
	year = {2024},
	note = {arXiv:2407.21783 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence},
	file = {arXiv Fulltext PDF:C\:\\Users\\320253692\\Zotero\\storage\\KWFANFYJ\\Dubey et al. - 2024 - The Llama 3 Herd of Models.pdf:application/pdf;arXiv.org Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\2MSUNAVE\\2407.html:text/html},
}

@article{li_multi-label_2023,
	title = {A {Multi}-{Label} {Detection} {Deep} {Learning} {Model} with {Attention}-{Guided} {Image} {Enhancement} for {Retinal} {Images}},
	volume = {14},
	issn = {2072-666X},
	abstract = {At present, multi-disease fundus image classification tasks still have the problems of small data volumes, uneven distributions, and low classification accuracy. In order to solve the problem of large data demand of deep learning models, a multi-disease fundus image classification ensemble model based on gradient-weighted class activation mapping (Grad-CAM) is proposed. The model uses VGG19 and ResNet50 as the classification networks. Grad-CAM is a data augmentation module used to obtain a network convolutional layer output activation map. Both the augmented and the original data are used as the input of the model to achieve the classification goal. The data augmentation module can guide the model to learn the feature differences of lesions in the fundus and enhance the robustness of the classification model. Model fine tuning and transfer learning are used to improve the accuracy of multiple classifiers. The proposed method is based on the RFMiD (Retinal Fundus Multi-Disease Image Dataset) dataset, and an ablation experiment was performed. Compared with other methods, the accuracy, precision, and recall of this model are 97\%, 92\%, and 81\%, respectively. The resulting activation graph shows the areas of interest for model classification, making it easier to understand the classification network.},
	number = {3},
	urldate = {2024-10-07},
	journal = {Micromachines},
	author = {Li, Zhenwei and Xu, Mengying and Yang, Xiaoli and Han, Yanqi and Wang, Jiawen},
	month = mar,
	year = {2023},
	pmid = {36985112},
	pmcid = {PMC10054796},
	pages = {705},
	file = {PubMed Central Full Text PDF:C\:\\Users\\320253692\\Zotero\\storage\\ZVPFVNX9\\Li et al. - 2023 - A Multi-Label Detection Deep Learning Model with A.pdf:application/pdf},
}

@misc{trinh_res-dense_2022,
	title = {Res-{Dense} {Net} for {3D} {Covid} {Chest} {CT}-scan classification},
	abstract = {One of the most contentious areas of research in Medical Image Preprocessing is 3D CT-scan. With the rapid spread of COVID-19, the function of CT-scan in properly and swiftly diagnosing the disease has become critical. It has a positive impact on infection prevention. There are many tasks to diagnose the illness through CT-scan images, include COVID-19. In this paper, we propose a method that using a Stacking Deep Neural Network to detect the Covid 19 through the series of 3D CT-scans images . In our method, we experiment with two backbones are DenseNet 121 and ResNet 101. This method achieves a competitive performance on some evaluation metrics},
	urldate = {2024-11-21},
	publisher = {arXiv},
	author = {Trinh, Quoc-Huy and Nguyen, Minh-Van and Dinh, Thien-Phuc Nguyen},
	month = aug,
	year = {2022},
	note = {arXiv:2208.04613},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Machine Learning},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\MDT2LDT3\\Trinh et al. - 2022 - Res-Dense Net for 3D Covid Chest CT-scan classific.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\SW7H2UDK\\2208.html:text/html},
}

@article{rainio_evaluation_2024,
	title = {Evaluation metrics and statistical tests for machine learning},
	volume = {14},
	copyright = {2024 The Author(s)},
	issn = {2045-2322},
	abstract = {Research on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not familiar with statistics, it might be difficult to understand how to evaluate the performance of ML models and compare them with each other. Here, we introduce the most common evaluation metrics used for the typical supervised ML tasks including binary, multi-class, and multi-label classification, regression, image segmentation, object detection, and information retrieval. We explain how to choose a suitable statistical test for comparing models, how to obtain enough values of the metric for testing, and how to perform the test and interpret its results. We also present a few practical examples about comparing convolutional neural networks used to classify X-rays with different lung infections and detect cancer tumors in positron emission tomography images.},
	language = {en},
	number = {1},
	urldate = {2024-11-22},
	journal = {Scientific Reports},
	author = {Rainio, Oona and Teuho, Jarmo and Klén, Riku},
	month = mar,
	year = {2024},
	note = {Publisher: Nature Publishing Group},
	keywords = {Computer science, Statistics},
	pages = {6086},
	file = {Full Text PDF:C\:\\Users\\320253692\\Zotero\\storage\\8FAQZPQN\\Rainio et al. - 2024 - Evaluation metrics and statistical tests for machi.pdf:application/pdf},
}

@misc{krzywda_graph_2022,
	title = {Graph {Neural} {Networks} in {Computer} {Vision} -- {Architectures}, {Datasets} and {Common} {Approaches}},
	abstract = {Graph Neural Networks (GNNs) are a family of graph networks inspired by mechanisms existing between nodes on a graph. In recent years there has been an increased interest in GNN and their derivatives, i.e., Graph Attention Networks (GAT), Graph Convolutional Networks (GCN), and Graph Recurrent Networks (GRN). An increase in their usability in computer vision is also observed. The number of GNN applications in this field continues to expand; it includes video analysis and understanding, action and behavior recognition, computational photography, image and video synthesis from zero or few shots, and many more. This contribution aims to collect papers published about GNN-based approaches towards computer vision. They are described and summarized from three perspectives. Firstly, we investigate the architectures of Graph Neural Networks and their derivatives used in this area to provide accurate and explainable recommendations for the ensuing investigations. As for the other aspect, we also present datasets used in these works. Finally, using graph analysis, we also examine relations between GNN-based studies in computer vision and potential sources of inspiration identified outside of this field.},
	urldate = {2024-12-03},
	publisher = {arXiv},
	author = {Krzywda, Maciej and Łukasik, Szymon and Gandomi, Amir H.},
	month = dec,
	year = {2022},
	note = {arXiv:2212.10207},
	keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\GY6IJCF7\\Krzywda et al. - 2022 - Graph Neural Networks in Computer Vision -- Archit.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\JKL72E58\\2212.html:text/html},
}

@article{noauthor_jax_nodate,
	title = {Jax {Reference}},
}

@misc{chen_vit-v-net_2021,
	title = {{ViT}-{V}-{Net}: {Vision} {Transformer} for {Unsupervised} {Volumetric} {Medical} {Image} {Registration}},
	shorttitle = {{ViT}-{V}-{Net}},
	abstract = {In the last decade, convolutional neural networks (ConvNets) have dominated and achieved state-of-the-art performances in a variety of medical imaging applications. However, the performances of ConvNets are still limited by lacking the understanding of long-range spatial relations in an image. The recently proposed Vision Transformer (ViT) for image classification uses a purely self-attention-based model that learns long-range spatial relations to focus on the relevant parts of an image. Nevertheless, ViT emphasizes the low-resolution features because of the consecutive downsamplings, result in a lack of detailed localization information, making it unsuitable for image registration. Recently, several ViT-based image segmentation methods have been combined with ConvNets to improve the recovery of detailed localization information. Inspired by them, we present ViT-V-Net, which bridges ViT and ConvNet to provide volumetric medical image registration. The experimental results presented here demonstrate that the proposed architecture achieves superior performance to several top-performing registration methods.},
	urldate = {2024-12-04},
	publisher = {arXiv},
	author = {Chen, Junyu and He, Yufan and Frey, Eric C. and Li, Ye and Du, Yong},
	month = apr,
	year = {2021},
	note = {arXiv:2104.06468 [eess]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing},
	file = {Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\4LDZSERG\\2104.html:text/html},
}

@incollection{patel_ct_2024,
	address = {Treasure Island (FL)},
	title = {{CT} {Scan}},
	copyright = {Copyright © 2024, StatPearls Publishing LLC.},
	abstract = {A computed tomography (CT) scan, commonly referred to as a CT, is a radiological imaging study. The machine was developed by physicist Allan MacLeod Cormack and electrical engineer Godfrey Hounsfield. Their development awarded them the Nobel Prize in Physiology or Medicine in 1979. The first scanners were installed in 1974. Since then, technological advances and math have allowed single images to be computed into two-dimensional informative images. The CT scan is essentially an x-ray study, where a series of rays are rotated around a specified body part, and computer-generated cross-sectional images are produced. The advantage of these tomographic images compared to conventional x-rays is that they contain detailed information about a specified area in cross-section, eliminating the superimposition of images, which provides a tremendous advantage over plain films. CT scans provide excellent clinicopathological correlation for a suspected illness.  CT scan augments the physician's ability to diagnose a patient's illness accurately. Low-dose CT scans are proving useful in preventative medicine and cancer screening. The study was initially called a CAT scan representing computer axial tomography, where the table moved after each axial image was obtained. In a spiral or helical scan, the table moves continuously as the x-ray source and detectors rotate. This reduces the duration of the study significantly to provide quick results in emergent situations. It rapidly substituted cerebral angiography for detecting head trauma injuries and brain masses in a fast and extremely reliable way. A radiologic technician acquires CT scans, which are interpreted and reported by a trained radiologist.},
	language = {eng},
	urldate = {2024-12-06},
	booktitle = {{StatPearls}},
	publisher = {StatPearls Publishing},
	author = {Patel, Paula R. and De Jesus, Orlando},
	year = {2024},
	pmid = {33620865},
	file = {Printable HTML:C\:\\Users\\320253692\\Zotero\\storage\\TZYV6JTV\\NBK567796.html:text/html},
}

@misc{gu_convformer_2022,
	title = {{ConvFormer}: {Combining} {CNN} and {Transformer} for {Medical} {Image} {Segmentation}},
	shorttitle = {{ConvFormer}},
	abstract = {Convolutional neural network (CNN) based methods have achieved great successes in medical image segmentation, but their capability to learn global representations is still limited due to using small effective receptive fields of convolution operations. Transformer based methods are capable of modelling long-range dependencies of information for capturing global representations, yet their ability to model local context is lacking. Integrating CNN and Transformer to learn both local and global representations while exploring multi-scale features is instrumental in further improving medical image segmentation. In this paper, we propose a hierarchical CNN and Transformer hybrid architecture, called ConvFormer, for medical image segmentation. ConvFormer is based on several simple yet effective designs. (1) A feed forward module of Deformable Transformer (DeTrans) is re-designed to introduce local information, called Enhanced DeTrans. (2) A residual-shaped hybrid stem based on a combination of convolutions and Enhanced DeTrans is developed to capture both local and global representations to enhance representation ability. (3) Our encoder utilizes the residual-shaped hybrid stem in a hierarchical manner to generate feature maps in different scales, and an additional Enhanced DeTrans encoder with residual connections is built to exploit multi-scale features with feature maps of different scales as input. Experiments on several datasets show that our ConvFormer, trained from scratch, outperforms various CNN- or Transformer-based architectures, achieving state-of-the-art performance.},
	urldate = {2024-12-06},
	publisher = {arXiv},
	author = {Gu, Pengfei and Zhang, Yejia and Wang, Chaoli and Chen, Danny Z.},
	month = nov,
	year = {2022},
	note = {arXiv:2211.08564 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\NSH84EH7\\Gu et al. - 2022 - ConvFormer Combining CNN and Transformer for Medi.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\6RW8T3GR\\2211.html:text/html},
}

@article{yuan_deep_2023,
	title = {Deep {Learning} {CT} {Image} {Restoration} using {System} {Blur} {Models}},
	volume = {12463},
	issn = {0277-786X},
	abstract = {Restoration of images contaminated by blur is an important processing tool across modalities including computed tomography where the blur induced by various system factors can be complex with dependencies on acquisition and reconstruction protocol, and even be patient-dependent. In many cases, such a blur can be modeled and predicted with high accuracy providing an important input to a classical deconvolution approach. While traditional deblurring methods tend to be highly noise magnifying, deep learning approaches have the potential to improve upon classic performance limits. However, most network architectures base their restoration on data inputs alone without knowledge of the system blur. In this work, we explore a deep learning approach that takes both image inputs as well as information that characterizes the system blur to combine modeling and deep learning approaches. We apply the approach to CT image restoration and compare with an image-only deep learning approach. We find that inclusion of the system blur model improves deblurring performance - suggesting the potential power of the combined modeling and deep learning technique.},
	urldate = {2024-12-06},
	journal = {Proceedings of SPIE--the International Society for Optical Engineering},
	author = {Yuan, Yijie and Tivnan, Matthew and Gang, Grace J. and Stayman, J. Webster},
	month = feb,
	year = {2023},
	pmid = {38170078},
	pmcid = {PMC10760795},
	pages = {124634J},
	file = {PubMed Central Full Text PDF:C\:\\Users\\320253692\\Zotero\\storage\\3TXYTXF4\\Yuan et al. - 2023 - Deep Learning CT Image Restoration using System Bl.pdf:application/pdf},
}

@article{rayed_deep_2024,
	title = {Deep learning for medical image segmentation: {State}-of-the-art advancements and challenges},
	volume = {47},
	issn = {2352-9148},
	shorttitle = {Deep learning for medical image segmentation},
	abstract = {Image segmentation, a crucial process of dividing images into distinct parts or objects, has witnessed remarkable advancements with the emergence of deep learning (DL) techniques. The use of layers in deep neural networks, like object form recognition in higher layers and basic edge identification in lower layers, has markedly improved the quality and accuracy of image segmentation. Consequently, DL using picture segmentation has become commonplace, video analysis, facial recognition, etc. Grasping the applications, algorithms, current performance, and challenges are crucial for advancing DL-based medical image segmentation. However, there is a lack of studies delving into the latest state-of-the-art developments in this field. Therefore, this survey aimed to thoroughly explore the most recent applications of DL-based medical image segmentation, encompassing an in-depth analysis of various commonly used datasets, pre-processing techniques and DL algorithms. This study also investigated the state-of-the-art advancement done in DL-based medical image segmentation by analyzing their results and experimental details. Finally, this study discussed the challenges and future research directions of DL-based medical image segmentation. Overall, this survey provides a comprehensive insight into DL-based medical image segmentation by covering its application domains, model exploration, analysis of state-of-the-art results, challenges, and research directions—a valuable resource for multidisciplinary studies.},
	urldate = {2024-12-06},
	journal = {Informatics in Medicine Unlocked},
	author = {Rayed, Md. Eshmam and Islam, S. M. Sajibul and Niha, Sadia Islam and Jim, Jamin Rahman and Kabir, Md Mohsin and Mridha, M. F.},
	month = jan,
	year = {2024},
	keywords = {Medical imaging, Deep learning, Data augmentation, Image pre-processing, Image segmentation},
	pages = {101504},
	file = {ScienceDirect Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\2LE9FM8J\\S2352914824000601.html:text/html},
}

@article{mazonakis_computed_2016,
	title = {Computed tomography: {What} and how does it measure?},
	volume = {85},
	issn = {1872-7727},
	shorttitle = {Computed tomography},
	abstract = {The current study provides a comprehensive review about the use and the clinical applications of computed tomography (CT) associated with the in vivo evaluation of the human body composition. The high-resolution CT images allow the accurate separation of the various body compartments at the tissue/organ level including adipose tissue, skeletal muscle, bones and organs. The further ability of the imaging modality to distinguish the cortical from the trabecular bone and the visceral from the susbcutaneous fat is of great value in clinical studies. CT may also give important information about the components of the subcutaneous adipose tissue and the muscle or liver fat infiltration. The efficient determination of the skeletal muscle attenuation and bone mineral density, that related with metabolic disorders, is feasible with the aid of CT data. The area and volume of each human body compartment may be estimated with high accuracy and reproducibility from CT scans. These estimations may be carried out using the methods of manual planimetry, semi-automatic segmentation of the tissue of interest, stereological point-counting approach and geometrical models based either on linear or area measurements. The advantages and disadvantages of the aforementioned methods for the quantification of the human body composition are presented and discussed.},
	language = {eng},
	number = {8},
	journal = {European Journal of Radiology},
	author = {Mazonakis, Michalis and Damilakis, John},
	month = aug,
	year = {2016},
	pmid = {26995675},
	keywords = {Humans, Reproducibility of Results, Tomography, X-Ray Computed, Computed tomography, Adipose Tissue, Body Composition, Bone and Bones, Bone Density, Human body compartments, Measurement methods, Muscle, Skeletal, Βody composition},
	pages = {1499--1504},
}

@article{ho_3d-cnn_2021,
	title = {A {3D}-{CNN} model with {CT}-based parametric response mapping for classifying {COPD} subjects},
	volume = {11},
	copyright = {2021 The Author(s)},
	issn = {2045-2322},
	abstract = {Chronic obstructive pulmonary disease (COPD) is a respiratory disorder involving abnormalities of lung parenchymal morphology with different severities. COPD is assessed by pulmonary-function tests and computed tomography-based approaches. We introduce a new classification method for COPD grouping based on deep learning and a parametric-response mapping (PRM) method. We extracted parenchymal functional variables of functional small airway disease percentage (fSAD\%) and emphysema percentage (Emph\%) with an image registration technique, being provided as input parameters of 3D convolutional neural network (CNN). The integrated 3D-CNN and PRM (3D-cPRM) achieved a classification accuracy of 89.3\% and a sensitivity of 88.3\% in five-fold cross-validation. The prediction accuracy of the proposed 3D-cPRM exceeded those of the 2D model and traditional 3D CNNs with the same neural network, and was comparable to that of 2D pretrained PRM models. We then applied a gradient-weighted class activation mapping (Grad-CAM) that highlights the key features in the CNN learning process. Most of the class-discriminative regions appeared in the upper and middle lobes of the lung, consistent with the regions of elevated fSAD\% and Emph\% in COPD subjects. The 3D-cPRM successfully represented the parenchymal abnormalities in COPD and matched the CT-based diagnosis of COPD.},
	language = {en},
	number = {1},
	urldate = {2024-12-06},
	journal = {Scientific Reports},
	author = {Ho, Thao Thi and Kim, Taewoo and Kim, Woo Jin and Lee, Chang Hyun and Chae, Kum Ju and Bak, So Hyeon and Kwon, Sung Ok and Jin, Gong Yong and Park, Eun-Kee and Choi, Sanghun},
	month = jan,
	year = {2021},
	note = {Publisher: Nature Publishing Group},
	keywords = {Biomedical engineering, Computational models, Image processing, Imaging},
	pages = {34},
	file = {Full Text:C\:\\Users\\320253692\\Zotero\\storage\\2FWL635T\\Ho et al. - 2021 - A 3D-CNN model with CT-based parametric response m.pdf:application/pdf},
}

@article{panwar_deep_2020,
	title = {A deep learning and grad-{CAM} based color visualization approach for fast detection of {COVID}-19 cases using chest {X}-ray and {CT}-{Scan} images},
	volume = {140},
	issn = {0960-0779},
	abstract = {The world is suffering from an existential global health crisis known as the COVID-19 pandemic. Countries like India, Bangladesh, and other developing countries are still having a slow pace in the detection of COVID-19 cases. Therefore, there is an urgent need for fast detection with clear visualization of infection is required using which a suspected patient of COVID-19 could be saved. In the recent technological advancements, the fusion of deep learning classifiers and medical images provides more promising results corresponding to traditional RT-PCR testing while making detection and predictions about COVID-19 cases with increased accuracy. In this paper, we have proposed a deep transfer learning algorithm that accelerates the detection of COVID-19 cases by using X-ray and CT-Scan images of the chest. It is because, in COVID-19, initial screening of chest X-ray (CXR) may provide significant information in the detection of suspected COVID-19 cases. We have considered three datasets known as 1) COVID-chest X-ray, 2) SARS-COV-2 CT-scan, and 3) Chest X-Ray Images (Pneumonia). In the obtained results, the proposed deep learning model can detect the COVID-19 positive cases in  ≤  2 seconds which is faster than RT-PCR tests currently being used for detection of COVID-19 cases. We have also established a relationship between COVID-19 patients along with the Pneumonia patients which explores the pattern between Pneumonia and COVID-19 radiology images. In all the experiments, we have used the Grad-CAM based color visualization approach in order to clearly interpretate the detection of radiology images and taking further course of action.},
	urldate = {2024-12-13},
	journal = {Chaos, Solitons, and Fractals},
	author = {Panwar, Harsh and Gupta, P.K. and Siddiqui, Mohammad Khubeb and Morales-Menendez, Ruben and Bhardwaj, Prakhar and Singh, Vaishnavi},
	month = nov,
	year = {2020},
	pmid = {32836918},
	pmcid = {PMC7413068},
	pages = {110190},
	file = {PubMed Central Full Text PDF:C\:\\Users\\320253692\\Zotero\\storage\\TX5FY4WY\\Panwar et al. - 2020 - A deep learning and grad-CAM based color visualiza.pdf:application/pdf},
}

@misc{bechler-speicher_intelligible_2024,
	title = {The {Intelligible} and {Effective} {Graph} {Neural} {Additive} {Networks}},
	abstract = {Graph Neural Networks (GNNs) have emerged as the predominant approach for learning over graph-structured data. However, most GNNs operate as black-box models and require post-hoc explanations, which may not suffice in high-stakes scenarios where transparency is crucial. In this paper, we present a GNN that is interpretable by design. Our model, Graph Neural Additive Network (GNAN), is a novel extension of the interpretable class of Generalized Additive Models, and can be visualized and fully understood by humans. GNAN is designed to be fully interpretable, offering both global and local explanations at the feature and graph levels through direct visualization of the model. These visualizations describe exactly how the model uses the relationships between the target variable, the features, and the graph. We demonstrate the intelligibility of GNANs in a series of examples on different tasks and datasets. In addition, we show that the accuracy of GNAN is on par with black-box GNNs, making it suitable for critical applications where transparency is essential, alongside high accuracy.},
	urldate = {2024-12-20},
	publisher = {arXiv},
	author = {Bechler-Speicher, Maya and Globerson, Amir and Gilad-Bachrach, Ran},
	month = dec,
	year = {2024},
	note = {arXiv:2406.01317 [cs]},
	keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\RTLWH2QA\\Bechler-Speicher et al. - 2024 - The Intelligible and Effective Graph Neural Additi.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\YLBVITJZ\\2406.html:text/html},
}

@misc{brody_how_2022,
	title = {How {Attentive} are {Graph} {Attention} {Networks}?},
	abstract = {Graph Attention Networks (GATs) are one of the most popular GNN architectures and are considered as the state-of-the-art architecture for representation learning with graphs. In GAT, every node attends to its neighbors given its own representation as the query. However, in this paper we show that GAT computes a very limited kind of attention: the ranking of the attention scores is unconditioned on the query node. We formally define this restricted kind of attention as static attention and distinguish it from a strictly more expressive dynamic attention. Because GATs use a static attention mechanism, there are simple graph problems that GAT cannot express: in a controlled problem, we show that static attention hinders GAT from even fitting the training data. To remove this limitation, we introduce a simple fix by modifying the order of operations and propose GATv2: a dynamic graph attention variant that is strictly more expressive than GAT. We perform an extensive evaluation and show that GATv2 outperforms GAT across 11 OGB and other benchmarks while we match their parametric costs. Our code is available at https://github.com/tech-srl/how\_attentive\_are\_gats . GATv2 is available as part of the PyTorch Geometric library, the Deep Graph Library, and the TensorFlow GNN library.},
	urldate = {2024-12-22},
	publisher = {arXiv},
	author = {Brody, Shaked and Alon, Uri and Yahav, Eran},
	month = jan,
	year = {2022},
	note = {arXiv:2105.14491 [cs]},
	keywords = {Computer Science - Machine Learning},
	annote = {Comment: Published in ICLR 2022},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\MFCTMGZG\\Brody et al. - 2022 - How Attentive are Graph Attention Networks.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\ZM9HLR9Y\\2105.html:text/html},
}

@misc{warner_smarter_2024,
	title = {Smarter, {Better}, {Faster}, {Longer}: {A} {Modern} {Bidirectional} {Encoder} for {Fast}, {Memory} {Efficient}, and {Long} {Context} {Finetuning} and {Inference}},
	shorttitle = {Smarter, {Better}, {Faster}, {Longer}},
	abstract = {Encoder-only transformer models such as BERT offer a great performance-size tradeoff for retrieval and classification tasks with respect to larger decoder-only models. Despite being the workhorse of numerous production pipelines, there have been limited Pareto improvements to BERT since its release. In this paper, we introduce ModernBERT, bringing modern model optimizations to encoder-only models and representing a major Pareto improvement over older encoders. Trained on 2 trillion tokens with a native 8192 sequence length, ModernBERT models exhibit state-of-the-art results on a large pool of evaluations encompassing diverse classification tasks and both single and multi-vector retrieval on different domains (including code). In addition to strong downstream performance, ModernBERT is also the most speed and memory efficient encoder and is designed for inference on common GPUs.},
	urldate = {2024-12-27},
	publisher = {arXiv},
	author = {Warner, Benjamin and Chaffin, Antoine and Clavié, Benjamin and Weller, Orion and Hallström, Oskar and Taghadouini, Said and Gallagher, Alexis and Biswas, Raja and Ladhak, Faisal and Aarsen, Tom and Cooper, Nathan and Adams, Griffin and Howard, Jeremy and Poli, Iacopo},
	month = dec,
	year = {2024},
	note = {arXiv:2412.13663 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Artificial Intelligence},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\DBCUQ766\\Warner et al. - 2024 - Smarter, Better, Faster, Longer A Modern Bidirect.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\2K74ULE3\\2412.html:text/html},
}

@misc{deng_mvketr_2025,
	title = {{MvKeTR}: {Chest} {CT} {Report} {Generation} with {Multi}-{View} {Perception} and {Knowledge} {Enhancement}},
	shorttitle = {{MvKeTR}},
	abstract = {CT report generation (CTRG) aims to automatically generate diagnostic reports for 3D volumes, relieving clinicians' workload and improving patient care. Despite clinical value, existing works fail to effectively incorporate diagnostic information from multiple anatomical views and lack related clinical expertise essential for accurate and reliable diagnosis. To resolve these limitations, we propose a novel Multi-view perception Knowledge-enhanced Transformer (MvKeTR) to mimic the diagnostic workflow of clinicians. Just as radiologists first examine CT scans from multiple planes, a Multi-View Perception Aggregator (MVPA) with view-aware attention effectively synthesizes diagnostic information from multiple anatomical views. Then, inspired by how radiologists further refer to relevant clinical records to guide diagnostic decision-making, a Cross-Modal Knowledge Enhancer (CMKE) retrieves the most similar reports based on the query volume to incorporate domain knowledge into the diagnosis procedure. Furthermore, instead of traditional MLPs, we employ Kolmogorov-Arnold Networks (KANs) with learnable nonlinear activation functions as the fundamental building blocks of both modules to better capture intricate diagnostic patterns in CT interpretation. Extensive experiments on the public CTRG-Chest-548K dataset demonstrate that our method outpaces prior state-of-the-art (SOTA) models across almost all metrics. The code will be made publicly available.},
	urldate = {2025-01-09},
	publisher = {arXiv},
	author = {Deng, Xiwei and He, Xianchun and Bao, Jiangfeng and Zhou, Yudan and Cai, Shuhui and Cai, Congbo and Chen, Zhong},
	month = jan,
	year = {2025},
	note = {arXiv:2411.18309 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence},
	annote = {Comment: 11 pages, 10 figures},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\668A2SBX\\Deng et al. - 2025 - MvKeTR Chest CT Report Generation with Multi-View.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\VH2N8HI4\\2411.html:text/html},
}

@misc{liu_video_2021,
	title = {Video {Swin} {Transformer}},
	abstract = {The vision community is witnessing a modeling shift from CNNs to Transformers, where pure Transformer architectures have attained top accuracy on the major video recognition benchmarks. These video models are all built on Transformer layers that globally connect patches across the spatial and temporal dimensions. In this paper, we instead advocate an inductive bias of locality in video Transformers, which leads to a better speed-accuracy trade-off compared to previous approaches which compute self-attention globally even with spatial-temporal factorization. The locality of the proposed video architecture is realized by adapting the Swin Transformer designed for the image domain, while continuing to leverage the power of pre-trained image models. Our approach achieves state-of-the-art accuracy on a broad range of video recognition benchmarks, including on action recognition (84.9 top-1 accuracy on Kinetics-400 and 86.1 top-1 accuracy on Kinetics-600 with {\textasciitilde}20x less pre-training data and {\textasciitilde}3x smaller model size) and temporal modeling (69.6 top-1 accuracy on Something-Something v2). The code and models will be made publicly available at https://github.com/SwinTransformer/Video-Swin-Transformer.},
	urldate = {2025-01-13},
	publisher = {arXiv},
	author = {Liu, Ze and Ning, Jia and Cao, Yue and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Hu, Han},
	month = jun,
	year = {2021},
	note = {arXiv:2106.13230 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Artificial Intelligence},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\7Q3XQW39\\Liu et al. - 2021 - Video Swin Transformer.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\9QCX9GQQ\\2106.html:text/html},
}

@misc{yang_swin3d_2023,
	title = {{Swin3D}: {A} {Pretrained} {Transformer} {Backbone} for {3D} {Indoor} {Scene} {Understanding}},
	shorttitle = {{Swin3D}},
	abstract = {The use of pretrained backbones with fine-tuning has been successful for 2D vision and natural language processing tasks, showing advantages over task-specific networks. In this work, we introduce a pretrained 3D backbone, called \{{\textbackslash}SST\}, for 3D indoor scene understanding. We design a 3D Swin transformer as our backbone network, which enables efficient self-attention on sparse voxels with linear memory complexity, making the backbone scalable to large models and datasets. We also introduce a generalized contextual relative positional embedding scheme to capture various irregularities of point signals for improved network performance. We pretrained a large \{{\textbackslash}SST\} model on a synthetic Structured3D dataset, which is an order of magnitude larger than the ScanNet dataset. Our model pretrained on the synthetic dataset not only generalizes well to downstream segmentation and detection on real 3D point datasets, but also outperforms state-of-the-art methods on downstream tasks with +2.3 mIoU and +2.2 mIoU on S3DIS Area5 and 6-fold semantic segmentation, +1.8 mIoU on ScanNet segmentation (val), +1.9 mAP@0.5 on ScanNet detection, and +8.1 mAP@0.5 on S3DIS detection. A series of extensive ablation studies further validate the scalability, generality, and superior performance enabled by our approach. The code and models are available at https://github.com/microsoft/Swin3D .},
	urldate = {2025-01-20},
	publisher = {arXiv},
	author = {Yang, Yu-Qi and Guo, Yu-Xiao and Xiong, Jian-Yu and Liu, Yang and Pan, Hao and Wang, Peng-Shuai and Tong, Xin and Guo, Baining},
	month = aug,
	year = {2023},
	note = {arXiv:2304.06906 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	annote = {Comment: Project page: https://yukichiii.github.io/project/swin3D/swin3D.html},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\URNMTFA8\\Yang et al. - 2023 - Swin3D A Pretrained Transformer Backbone for 3D I.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\E77RZNAE\\2304.html:text/html},
}

@misc{russakovsky_imagenet_2015,
	title = {{ImageNet} {Large} {Scale} {Visual} {Recognition} {Challenge}},
	abstract = {The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the five years of the challenge, and propose future directions and improvements.},
	urldate = {2025-01-21},
	publisher = {arXiv},
	author = {Russakovsky, Olga and Deng, Jia and Su, Hao and Krause, Jonathan and Satheesh, Sanjeev and Ma, Sean and Huang, Zhiheng and Karpathy, Andrej and Khosla, Aditya and Bernstein, Michael and Berg, Alexander C. and Fei-Fei, Li},
	month = jan,
	year = {2015},
	note = {arXiv:1409.0575 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	annote = {Comment: 43 pages, 16 figures. v3 includes additional comparisons with PASCAL VOC (per-category comparisons in Table 3, distribution of localization difficulty in Fig 16), a list of queries used for obtaining object detection images (Appendix C), and some additional references},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\429ZQ2QA\\Russakovsky et al. - 2015 - ImageNet Large Scale Visual Recognition Challenge.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\RQWY8LBY\\1409.html:text/html},
}

@misc{morris_weisfeiler_2021,
	title = {Weisfeiler and {Leman} {Go} {Neural}: {Higher}-order {Graph} {Neural} {Networks}},
	shorttitle = {Weisfeiler and {Leman} {Go} {Neural}},
	abstract = {In recent years, graph neural networks (GNNs) have emerged as a powerful neural architecture to learn vector representations of nodes and graphs in a supervised, end-to-end fashion. Up to now, GNNs have only been evaluated empirically -- showing promising results. The following work investigates GNNs from a theoretical point of view and relates them to the \$1\$-dimensional Weisfeiler-Leman graph isomorphism heuristic (\$1\$-WL). We show that GNNs have the same expressiveness as the \$1\$-WL in terms of distinguishing non-isomorphic (sub-)graphs. Hence, both algorithms also have the same shortcomings. Based on this, we propose a generalization of GNNs, so-called \$k\$-dimensional GNNs (\$k\$-GNNs), which can take higher-order graph structures at multiple scales into account. These higher-order structures play an essential role in the characterization of social networks and molecule graphs. Our experimental evaluation confirms our theoretical findings as well as confirms that higher-order information is useful in the task of graph classification and regression.},
	urldate = {2025-01-26},
	publisher = {arXiv},
	author = {Morris, Christopher and Ritzert, Martin and Fey, Matthias and Hamilton, William L. and Lenssen, Jan Eric and Rattan, Gaurav and Grohe, Martin},
	month = nov,
	year = {2021},
	note = {arXiv:1810.02244 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Computer Science - Artificial Intelligence, Statistics - Machine Learning},
	annote = {Comment: Extended version with proofs, accepted at AAAI 2019, added units of measurement of QM9 dataset into appendix, removed results from Wu et al., 2018 due to different units},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\SUQZ3HDE\\Morris et al. - 2021 - Weisfeiler and Leman Go Neural Higher-order Graph.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\YNJWUY5L\\1810.html:text/html},
}

@misc{kipf_semi-supervised_2017,
	title = {Semi-{Supervised} {Classification} with {Graph} {Convolutional} {Networks}},
	abstract = {We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin.},
	urldate = {2025-01-26},
	publisher = {arXiv},
	author = {Kipf, Thomas N. and Welling, Max},
	month = feb,
	year = {2017},
	note = {arXiv:1609.02907 [cs]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	annote = {Comment: Published as a conference paper at ICLR 2017},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\F8KJWDJ8\\Kipf and Welling - 2017 - Semi-Supervised Classification with Graph Convolut.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\VN97HZB7\\1609.html:text/html},
}

@article{velickovic_everything_2023,
	title = {Everything is {Connected}: {Graph} {Neural} {Networks}},
	volume = {79},
	issn = {0959440X},
	shorttitle = {Everything is {Connected}},
	abstract = {In many ways, graphs are the main modality of data we receive from nature. This is due to the fact that most of the patterns we see, both in natural and artificial systems, are elegantly representable using the language of graph structures. Prominent examples include molecules (represented as graphs of atoms and bonds), social networks and transportation networks. This potential has already been seen by key scientific and industrial groups, with already-impacted application areas including traffic forecasting, drug discovery, social network analysis and recommender systems. Further, some of the most successful domains of application for machine learning in previous years -- images, text and speech processing -- can be seen as special cases of graph representation learning, and consequently there has been significant exchange of information between these areas. The main aim of this short survey is to enable the reader to assimilate the key concepts in the area, and position graph representation learning in a proper context with related fields.},
	urldate = {2025-01-27},
	journal = {Current Opinion in Structural Biology},
	author = {Veličković, Petar},
	month = apr,
	year = {2023},
	note = {arXiv:2301.08210 [cs]},
	keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning, Computer Science - Social and Information Networks},
	pages = {102538},
	annote = {Comment: To appear in Current Opinion in Structural Biology. 14 pages, 1 figure},
	file = {Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\PTBDBRIE\\2301.html:text/html;Submitted Version:C\:\\Users\\320253692\\Zotero\\storage\\SAQB6W28\\Veličković - 2023 - Everything is Connected Graph Neural Networks.pdf:application/pdf},
}

@article{fan_optimizing_2023,
	series = {3rd {International} {Conference} on {Machine} {Learning} and {Big} {Data} {Analytics} for {IoT} {Security} and {Privacy}},
	title = {Optimizing {Transportation} {Management} with a {Hybrid} {GNN}-{RL} {Approach}},
	volume = {228},
	issn = {1877-0509},
	abstract = {The process of rapid urbanization in contemporary cities has resulted in a rise in traffic congestion, accidents, and pollution, leading to negative impacts on transportation efficiency and urban development. In response, Intelligent Transportation Systems (ITS) have emerged as a crucial field of research and development, with a goal to leverage technological advancements to enhance transportation safety, efficiency, and sustainability. However, traditional expert systems and traffic control methods have displayed limitations in handling the complexities of urban transportation. To address these challenges in Advanced Traffic Management Systems (ATMSs), this paper pro- poses a new approach utilizing Graph Neural Network-Reinforcement Learning (GNN-RL). By using GNNs for pre-training and information aggregation from the entire grid, our method enables downstream RL tasks, allowing for fine-tuning of effective global information aggregation and dynamic traffic signal control policy adjustment. The GNN-RL approach offers enhanced adapt- ability and performance in managing long-term dependencies, rendering it a promising alternative for traffic control in contemporary urban transportation systems.},
	urldate = {2025-01-27},
	journal = {Procedia Computer Science},
	author = {Fan, Ruochen},
	month = jan,
	year = {2023},
	keywords = {Graph Neural Network-Reinforcement Learning, Intelligent Transportation Systems, Transportation Management},
	pages = {755--761},
	file = {ScienceDirect Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\MJCT2N8C\\S1877050923019208.html:text/html},
}

@article{zhang_graph_2023,
	title = {Graph neural networks for image-guided disease diagnosis: {A} review},
	volume = {1},
	copyright = {© 2023 The Authors. iRADIOLOGY published by John Wiley \& Sons Australia, Ltd on behalf of Tsinghua University Press.},
	issn = {2834-2879},
	shorttitle = {Graph neural networks for image-guided disease diagnosis},
	abstract = {Medical imaging is playing an increasingly crucial role in disease diagnosis. Numerous deep learning-based methods have been developed for image-guided automatic disease diagnosis. Most of the methods have harnessed conventional convolutional neural networks, which are directly applied in the regular image domain. However, some irregular spatial patterns revealed in medical images are also critical to disease diagnosis, since they can describe latent relations in different image regions of a subject (e.g., different focal lesions in an image) or between different groups (e.g., Alzheimer's disease and healthy control). Therefore, how to exploit and analyze irregular spatial patterns and their relations has become a research challenge in the field of image-guided disease diagnosis. To address this challenge, graph neural networks (GNNs) are proposed to perform the convolution operation on graphs. Graphs can naturally represent irregular spatial structures. Because of their ability to aggregate node features, edge features, and graph structure information to capture and learn hidden spatial patterns in irregular structures, GNN-based algorithms have achieved promising results in the detection of various diseases. In this paper, we introduce commonly used GNN-based algorithms and systematically review their applications to disease diagnosis. We summarize the workflow of GNN-based applications in disease diagnosis, ranging from localizing the regions of interest and edge construction to modeling. Furthermore, we discuss the limitations and outline potential research directions for GNNs in disease diagnosis.},
	language = {en},
	number = {2},
	urldate = {2025-01-27},
	journal = {iRADIOLOGY},
	author = {Zhang, Lin and Zhao, Yan and Che, Tongtong and Li, Shuyu and Wang, Xiuying},
	year = {2023},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/ird3.20},
	keywords = {disease diagnosis, graph neural networks, medical imaging, review},
	pages = {151--166},
	file = {Full Text PDF:C\:\\Users\\320253692\\Zotero\\storage\\VGA6T64U\\Zhang et al. - 2023 - Graph neural networks for image-guided disease dia.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\XCP6ZKLK\\ird3.html:text/html},
}

@article{ahmedt-aristizabal_graph-based_2021,
	title = {Graph-{Based} {Deep} {Learning} for {Medical} {Diagnosis} and {Analysis}: {Past}, {Present} and {Future}},
	volume = {21},
	issn = {1424-8220},
	shorttitle = {Graph-{Based} {Deep} {Learning} for {Medical} {Diagnosis} and {Analysis}},
	abstract = {With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data. A major limitation of existing methods has been the focus on grid-like data; however, the structure of physiological recordings are often irregular and unordered, which makes it difficult to conceptualise them as a matrix. As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interacting nodes connected by edges whose weights can be determined by either temporal associations or anatomical junctions. In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare. We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure, and electrical-based analysis. We also outline the limitations of existing techniques and discuss potential directions for future research.},
	number = {14},
	urldate = {2025-01-27},
	journal = {Sensors (Basel, Switzerland)},
	author = {Ahmedt-Aristizabal, David and Armin, Mohammad Ali and Denman, Simon and Fookes, Clinton and Petersson, Lars},
	month = jul,
	year = {2021},
	pmid = {34300498},
	pmcid = {PMC8309939},
	pages = {4758},
	file = {PubMed Central Full Text PDF:C\:\\Users\\320253692\\Zotero\\storage\\N4N3E24P\\Ahmedt-Aristizabal et al. - 2021 - Graph-Based Deep Learning for Medical Diagnosis an.pdf:application/pdf},
}

@misc{wang_tacticai_2023,
	title = {{TacticAI}: an {AI} assistant for football tactics},
	shorttitle = {{TacticAI}},
	abstract = {Identifying key patterns of tactics implemented by rival teams, and developing effective responses, lies at the heart of modern football. However, doing so algorithmically remains an open research challenge. To address this unmet need, we propose TacticAI, an AI football tactics assistant developed and evaluated in close collaboration with domain experts from Liverpool FC. We focus on analysing corner kicks, as they offer coaches the most direct opportunities for interventions and improvements. TacticAI incorporates both a predictive and a generative component, allowing the coaches to effectively sample and explore alternative player setups for each corner kick routine and to select those with the highest predicted likelihood of success. We validate TacticAI on a number of relevant benchmark tasks: predicting receivers and shot attempts and recommending player position adjustments. The utility of TacticAI is validated by a qualitative study conducted with football domain experts at Liverpool FC. We show that TacticAI's model suggestions are not only indistinguishable from real tactics, but also favoured over existing tactics 90\% of the time, and that TacticAI offers an effective corner kick retrieval system. TacticAI achieves these results despite the limited availability of gold-standard data, achieving data efficiency through geometric deep learning.},
	urldate = {2025-01-27},
	publisher = {arXiv},
	author = {Wang, Zhe and Veličković, Petar and Hennes, Daniel and Tomašev, Nenad and Prince, Laurel and Kaisers, Michael and Bachrach, Yoram and Elie, Romuald and Wenliang, Li Kevin and Piccinini, Federico and Spearman, William and Graham, Ian and Connor, Jerome and Yang, Yi and Recasens, Adrià and Khan, Mina and Beauguerlange, Nathalie and Sprechmann, Pablo and Moreno, Pol and Heess, Nicolas and Bowling, Michael and Hassabis, Demis and Tuyls, Karl},
	month = oct,
	year = {2023},
	note = {arXiv:2310.10553 [cs]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Multiagent Systems},
	annote = {Comment: 32 pages, 10 figures},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\GJ4JLVIG\\Wang et al. - 2023 - TacticAI an AI assistant for football tactics.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\33GQQB2A\\2310.html:text/html},
}

@misc{makarov_graph_2024,
	title = {Graph neural network surrogate for strategic transport planning},
	abstract = {As the complexities of urban environments continue to grow, the modelling of transportation systems become increasingly challenging. This paper explores the application of advanced Graph Neural Network (GNN) architectures as surrogate models for strategic transport planning. Building upon a prior work that laid the foundation with graph convolution networks (GCN), our study delves into the comparative analysis of established GCN with the more expressive Graph Attention Network (GAT). Additionally, we propose a novel GAT variant (namely GATv3) to address over-smoothing issues in graph-based models. Our investigation also includes the exploration of a hybrid model combining both GCN and GAT architectures, aiming to investigate the performance of the mixture. The three models are applied to various experiments to understand their limits. We analyse hierarchical regression setups, combining classification and regression tasks, and introduce fine-grained classification with a proposal of a method to convert outputs to precise values. Results reveal the superior performance of the new GAT in classification tasks. To the best of the authors' knowledge, this is the first GAT model in literature to achieve larger depths. Surprisingly, the fine-grained classification task demonstrates the GCN's unexpected dominance with additional training data. This shows that synthetic data generators can increase the training data, without overfitting issues whilst improving model performance. In conclusion, this research advances GNN based surrogate modelling, providing insights for refining GNN architectures. The findings open avenues for investigating the potential of the newly proposed GAT architecture and the modelling setups for other transportation problems.},
	urldate = {2025-01-27},
	publisher = {arXiv},
	author = {Makarov, Nikita and Narayanan, Santhanakrishnan and Antoniou, Constantinos},
	month = aug,
	year = {2024},
	note = {arXiv:2408.07726 [cs]},
	keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\NLTSNRCP\\Makarov et al. - 2024 - Graph neural network surrogate for strategic trans.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\MKK7VDD3\\2408.html:text/html},
}

@article{reiser_graph_2022,
	title = {Graph neural networks for materials science and chemistry},
	volume = {3},
	copyright = {2022 The Author(s)},
	issn = {2662-4443},
	abstract = {Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this Review, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs.},
	language = {en},
	number = {1},
	urldate = {2025-01-27},
	journal = {Communications Materials},
	author = {Reiser, Patrick and Neubert, Marlen and Eberhard, André and Torresi, Luca and Zhou, Chen and Shao, Chen and Metni, Houssam and van Hoesel, Clint and Schopmans, Henrik and Sommer, Timo and Friederich, Pascal},
	month = nov,
	year = {2022},
	note = {Publisher: Nature Publishing Group},
	keywords = {Computer science, Scientific data, Atomistic models, Computational methods, Method development},
	pages = {1--18},
	file = {Full Text PDF:C\:\\Users\\320253692\\Zotero\\storage\\G5FYNFXW\\Reiser et al. - 2022 - Graph neural networks for materials science and ch.pdf:application/pdf},
}

@article{zhou_graph_2020,
	title = {Graph neural networks: {A} review of methods and applications},
	volume = {1},
	issn = {2666-6510},
	shorttitle = {Graph neural networks},
	abstract = {Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein interface, and classifying diseases demand a model to learn from graph inputs. In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures (like the dependency trees of sentences and the scene graphs of images) is an important research topic which also needs graph reasoning models. Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs. In recent years, variants of GNNs such as graph convolutional network (GCN), graph attention network (GAT), graph recurrent network (GRN) have demonstrated ground-breaking performances on many deep learning tasks. In this survey, we propose a general design pipeline for GNN models and discuss the variants of each component, systematically categorize the applications, and propose four open problems for future research.},
	urldate = {2025-01-27},
	journal = {AI Open},
	author = {Zhou, Jie and Cui, Ganqu and Hu, Shengding and Zhang, Zhengyan and Yang, Cheng and Liu, Zhiyuan and Wang, Lifeng and Li, Changcheng and Sun, Maosong},
	month = jan,
	year = {2020},
	keywords = {Deep learning, Graph neural network},
	pages = {57--81},
	file = {ScienceDirect Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\Q4AGJGU6\\S2666651021000012.html:text/html;Submitted Version:C\:\\Users\\320253692\\Zotero\\storage\\3US9W5ZN\\Zhou et al. - 2020 - Graph neural networks A review of methods and app.pdf:application/pdf},
}

@misc{li_evaluating_2023,
	title = {Evaluating {Graph} {Neural} {Networks} for {Link} {Prediction}: {Current} {Pitfalls} and {New} {Benchmarking}},
	shorttitle = {Evaluating {Graph} {Neural} {Networks} for {Link} {Prediction}},
	abstract = {Link prediction attempts to predict whether an unseen edge exists based on only a portion of edges of a graph. A flurry of methods have been introduced in recent years that attempt to make use of graph neural networks (GNNs) for this task. Furthermore, new and diverse datasets have also been created to better evaluate the effectiveness of these new models. However, multiple pitfalls currently exist that hinder our ability to properly evaluate these new methods. These pitfalls mainly include: (1) Lower than actual performance on multiple baselines, (2) A lack of a unified data split and evaluation metric on some datasets, and (3) An unrealistic evaluation setting that uses easy negative samples. To overcome these challenges, we first conduct a fair comparison across prominent methods and datasets, utilizing the same dataset and hyperparameter search settings. We then create a more practical evaluation setting based on a Heuristic Related Sampling Technique (HeaRT), which samples hard negative samples via multiple heuristics. The new evaluation setting helps promote new challenges and opportunities in link prediction by aligning the evaluation with real-world situations. Our implementation and data are available at https://github.com/Juanhui28/HeaRT},
	urldate = {2025-01-27},
	publisher = {arXiv},
	author = {Li, Juanhui and Shomer, Harry and Mao, Haitao and Zeng, Shenglai and Ma, Yao and Shah, Neil and Tang, Jiliang and Yin, Dawei},
	month = nov,
	year = {2023},
	note = {arXiv:2306.10453 [cs]},
	keywords = {Computer Science - Machine Learning, Computer Science - Social and Information Networks},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\CMH2K8XX\\Li et al. - 2023 - Evaluating Graph Neural Networks for Link Predicti.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\I9C9E534\\2306.html:text/html},
}

@article{maurya_simplifying_2022,
	title = {Simplifying approach to node classification in {Graph} {Neural} {Networks}},
	volume = {62},
	issn = {1877-7503},
	abstract = {Graph Neural Networks (GNNs) have become one of the indispensable tools to learn from graph-structured data, and their usefulness has been shown in wide variety of tasks. In recent years, there have been tremendous improvements in architecture design, resulting in better performance on various prediction tasks. In general, these neural architectures combine node feature aggregation and feature transformation using learnable weight matrix in the same layer. This makes it challenging to analyze the importance of node features aggregated from various hops and the expressiveness of the neural network layers. As different graph datasets show varying levels of homophily and heterophily in features and class label distribution, it becomes essential to understand which features are important for the prediction tasks without any prior information. In this work, we decouple the node feature aggregation step and depth of graph neural network, and empirically analyze how different aggregated features play a role in prediction performance. We show that not all features generated via aggregation steps are useful, and often using these less informative features can be detrimental to the performance of the GNN model. Through our experiments, we show that learning certain subsets of these features can lead to better performance on wide variety of datasets. Based on our observations, we introduce several key design strategies for graph neural networks. More specifically, we propose to use softmax as a regularizer and ”soft-selector” of features aggregated from neighbors at different hop distances; and L2-Normalization over GNN layers. Combining these techniques, we present a simple and shallow model, Feature Selection Graph Neural Network (FSGNN), and show empirically that the proposed model achieves comparable or even higher accuracy than state-of-the-art GNN models in nine benchmark datasets for the node classification task, with remarkable improvements up to 51.1\%. Source code available at https://github.com/sunilkmaurya/FSGNN/.},
	urldate = {2025-01-27},
	journal = {Journal of Computational Science},
	author = {Maurya, Sunil Kumar and Liu, Xin and Murata, Tsuyoshi},
	month = jul,
	year = {2022},
	keywords = {Feature selection, Graph Neural Networks, Node classification},
	pages = {101695},
	file = {ScienceDirect Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\WE8NQVLC\\S1877750322000990.html:text/html;Submitted Version:C\:\\Users\\320253692\\Zotero\\storage\\JKB823JL\\Maurya et al. - 2022 - Simplifying approach to node classification in Gra.pdf:application/pdf},
}

@article{anaya-isaza_overview_2021,
	title = {An overview of deep learning in medical imaging},
	volume = {26},
	issn = {2352-9148},
	abstract = {Deep learning (DL) is one of the branches of artificial intelligence that has seen exponential growth in recent years. The scientific community has focused its attention on DL due to its versatility, high performance, high generalization capacity, and multidisciplinary uses, among many other qualities. In addition, a large amount of medical data and the development of more powerful computers has also fostered an interest in this area. This paper presents an overview of current deep learning methods, starting from the most straightforward concept but accompanied by the mathematical models that are behind the functionality of this type of intelligence. In the first instance, the fundamental concept of artificial neural networks is introduced, progressively covering convolutional structures, recurrent networks, attention models, up to the current structure known as the Transformer. Secondly, all the basic concepts involved in training and other common elements in the design of the architectures are introduced. Thirdly, some of the key elements in modern networks for medical image classification and segmentation are shown. Subsequently, a review of some applications realized in the last years is shown, where the main features related to DL are highlighted. Finally, the perspectives and future expectations of deep learning are presented.},
	urldate = {2025-01-28},
	journal = {Informatics in Medicine Unlocked},
	author = {Anaya-Isaza, Andrés and Mera-Jiménez, Leonel and Zequera-Diaz, Martha},
	month = jan,
	year = {2021},
	keywords = {Medical imaging, Convolutional neural networks, Deep learning, Recurrent neural networks, Transformers, Attention models},
	pages = {100723},
	file = {ScienceDirect Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\TJFINA5G\\S2352914821002033.html:text/html},
}

@article{zhou_review_2021,
	title = {A review of deep learning in medical imaging: {Imaging} traits, technology trends, case studies with progress highlights, and future promises},
	volume = {109},
	issn = {0018-9219},
	shorttitle = {A review of deep learning in medical imaging},
	abstract = {Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high performance computing. However, medical imaging presents unique challenges that confront deep learning approaches. In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues. We cover the topics of network architecture, sparse and noisy labels, federating learning, interpretability, uncertainty quantification, etc. Then, we present several case studies that are commonly found in clinical practice, including digital pathology and chest, brain, cardiovascular, and abdominal imaging. Rather than presenting an exhaustive literature survey, we instead describe some prominent research highlights related to these case study applications. We conclude with a discussion and presentation of promising future directions.},
	number = {5},
	urldate = {2025-01-28},
	journal = {Proceedings of the IEEE. Institute of Electrical and Electronics Engineers},
	author = {Zhou, S. Kevin and Greenspan, Hayit and Davatzikos, Christos and Duncan, James S. and van Ginneken, Bram and Madabhushi, Anant and Prince, Jerry L. and Rueckert, Daniel and Summers, Ronald M.},
	month = may,
	year = {2021},
	pmid = {37786449},
	pmcid = {PMC10544772},
	pages = {820--838},
	file = {PubMed Central Full Text PDF:C\:\\Users\\320253692\\Zotero\\storage\\U86MU893\\Zhou et al. - 2021 - A review of deep learning in medical imaging Imag.pdf:application/pdf},
}

@misc{lou_large_2024,
	title = {Large {Language} {Model} {Instruction} {Following}: {A} {Survey} of {Progresses} and {Challenges}},
	shorttitle = {Large {Language} {Model} {Instruction} {Following}},
	abstract = {Task semantics can be expressed by a set of input-output examples or a piece of textual instruction. Conventional machine learning approaches for natural language processing (NLP) mainly rely on the availability of large-scale sets of task-specific examples. Two issues arise: first, collecting task-specific labeled examples does not apply to scenarios where tasks may be too complicated or costly to annotate, or the system is required to handle a new task immediately; second, this is not user-friendly since end-users are probably more willing to provide task description rather than a set of examples before using the system. Therefore, the community is paying increasing interest in a new supervision-seeking paradigm for NLP: learning to follow task instructions, i.e., instruction following. Despite its impressive progress, there are some common issues that the community struggles with. This survey paper tries to summarize and provide insights to the current research on instruction following, particularly, by answering the following questions: (i) What is task instruction, and what instruction types exist? (ii) How to model instructions? (iii) What are popular instruction following datasets and evaluation metrics? (iv) What factors influence and explain the instructions' performance? (v) What challenges remain in instruction following? To our knowledge, this is the first comprehensive survey about instruction following.},
	urldate = {2025-01-28},
	publisher = {arXiv},
	author = {Lou, Renze and Zhang, Kai and Yin, Wenpeng},
	month = may,
	year = {2024},
	note = {arXiv:2303.10475 [cs]
version: 8},
	keywords = {Computer Science - Computation and Language},
	annote = {Comment: Accepted by Computational Linguistics Journal. The paper list is available at https://github.com/RenzeLou/awesome-instruction-learning},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\8ULQSNTB\\Lou et al. - 2024 - Large Language Model Instruction Following A Surv.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\NWYJ4QLB\\2303.html:text/html},
}

@article{han_survey_2023,
	title = {A {Survey} on {Vision} {Transformer}},
	volume = {45},
	issn = {1939-3539},
	abstract = {Transformer, first applied to the field of natural language processing, is a type of deep neural network mainly based on the self-attention mechanism. Thanks to its strong representation capabilities, researchers are looking at ways to apply transformer to computer vision tasks. In a variety of visual benchmarks, transformer-based models perform similar to or better than other types of networks such as convolutional and recurrent neural networks. Given its high performance and less need for vision-specific inductive bias, transformer is receiving more and more attention from the computer vision community. In this paper, we review these vision transformer models by categorizing them in different tasks and analyzing their advantages and disadvantages. The main categories we explore include the backbone network, high/mid-level vision, low-level vision, and video processing. We also include efficient transformer methods for pushing transformer into real device-based applications. Furthermore, we also take a brief look at the self-attention mechanism in computer vision, as it is the base component in transformer. Toward the end of this paper, we discuss the challenges and provide several further research directions for vision transformers.},
	language = {eng},
	number = {1},
	journal = {IEEE transactions on pattern analysis and machine intelligence},
	author = {Han, Kai and Wang, Yunhe and Chen, Hanting and Chen, Xinghao and Guo, Jianyuan and Liu, Zhenhua and Tang, Yehui and Xiao, An and Xu, Chunjing and Xu, Yixing and Yang, Zhaohui and Zhang, Yiman and Tao, Dacheng},
	month = jan,
	year = {2023},
	pmid = {35180075},
	pages = {87--110},
	file = {Submitted Version:C\:\\Users\\320253692\\Zotero\\storage\\NP8YFGUI\\Han et al. - 2023 - A Survey on Vision Transformer.pdf:application/pdf},
}

@article{ibrahim_deep-chest_2021,
	title = {Deep-chest: {Multi}-classification deep learning model for diagnosing {COVID}-19, pneumonia, and lung cancer chest diseases},
	volume = {132},
	issn = {0010-4825},
	shorttitle = {Deep-chest},
	abstract = {Corona Virus Disease (COVID-19) has been announced as a pandemic and is spreading rapidly throughout the world. Early detection of COVID-19 may protect many infected people. Unfortunately, COVID-19 can be mistakenly diagnosed as pneumonia or lung cancer, which with fast spread in the chest cells, can lead to patient death. The most commonly used diagnosis methods for these three diseases are chest X-ray and computed tomography (CT) images. In this paper, a multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer from a combination of chest x-ray and CT images is proposed. This combination has been used because chest X-ray is less powerful in the early stages of the disease, while a CT scan of the chest is useful even before symptoms appear, and CT can precisely detect the abnormal features that are identified in images. In addition, using these two types of images will increase the dataset size, which will increase the classification accuracy. To the best of our knowledge, no other deep learning model choosing between these diseases is found in the literature. In the present work, the performance of four architectures are considered, namely: VGG19-CNN, ResNet152V2, ResNet152V2 + Gated Recurrent Unit (GRU), and ResNet152V2 + Bidirectional GRU (Bi-GRU). A comprehensive evaluation of different deep learning architectures is provided using public digital chest x-ray and CT datasets with four classes (i.e., Normal, COVID-19, Pneumonia, and Lung cancer). From the results of the experiments, it was found that the VGG19 +CNN model outperforms the three other proposed models. The VGG19+CNN model achieved 98.05\% accuracy (ACC), 98.05\% recall, 98.43\% precision, 99.5\% specificity (SPC), 99.3\% negative predictive value (NPV), 98.24\% F1 score, 97.7\% Matthew's correlation coefficient (MCC), and 99.66\% area under the curve (AUC) based on X-ray and CT images.},
	urldate = {2025-01-28},
	journal = {Computers in Biology and Medicine},
	author = {Ibrahim, Dina M. and Elshennawy, Nada M. and Sarhan, Amany M.},
	month = may,
	year = {2021},
	keywords = {Deep learning, Chest X-ray, COVID-19 detection, CT images, Lung cancer, Pneumonia},
	pages = {104348},
	file = {Full Text:C\:\\Users\\320253692\\Zotero\\storage\\RIQ4CNTX\\Ibrahim et al. - 2021 - Deep-chest Multi-classification deep learning mod.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\SZ6V3CDF\\S0010482521001426.html:text/html},
}

@article{azad_laplacian-former_2023,
	title = {Laplacian-{Former}: {Overcoming} the {Limitations} of {Vision} {Transformers} in {Local} {Texture} {Detection}},
	volume = {14222},
	shorttitle = {Laplacian-{Former}},
	abstract = {Vision Transformer (ViT) models have demonstrated a breakthrough in a wide range of computer vision tasks. However, compared to the Convolutional Neural Network (CNN) models, it has been observed that the ViT models struggle to capture high-frequency components of images, which can limit their ability to detect local textures and edge information. As abnormalities in human tissue, such as tumors and lesions, may greatly vary in structure, texture, and shape, high-frequency information such as texture is crucial for effective semantic segmentation tasks. To address this limitation in ViT models, we propose a new technique, Laplacian-Former, that enhances the self-attention map by adaptively re-calibrating the frequency information in a Laplacian pyramid. More specifically, our proposed method utilizes a dual attention mechanism via efficient attention and frequency attention while the efficient attention mechanism reduces the complexity of self-attention to linear while producing the same output, selectively intensifying the contribution of shape and texture features. Furthermore, we introduce a novel efficient enhancement multi-scale bridge that effectively transfers spatial information from the encoder to the decoder while preserving the fundamental features. We demonstrate the efficacy of Laplacian-former on multi-organ and skin lesion segmentation tasks with +1.87\% and +0.76\% dice scores compared to SOTA approaches, respectively. Our implementation is publically available at GitHub.},
	urldate = {2025-01-28},
	journal = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
	author = {Azad, Reza and Kazerouni, Amirhossein and Azad, Babak and Aghdam, Ehsan Khodapanah and Velichko, Yury and Bagci, Ulas and Merhof, Dorit},
	month = oct,
	year = {2023},
	pmid = {38299070},
	pmcid = {PMC10830169},
	pages = {736--746},
	file = {PubMed Central Full Text PDF:C\:\\Users\\320253692\\Zotero\\storage\\ZS9QK9VA\\Azad et al. - 2023 - Laplacian-Former Overcoming the Limitations of Vi.pdf:application/pdf},
}

@article{hosny_artificial_2018,
	title = {Artificial intelligence in radiology},
	volume = {18},
	issn = {1474-1768},
	abstract = {Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.},
	language = {eng},
	number = {8},
	journal = {Nature Reviews. Cancer},
	author = {Hosny, Ahmed and Parmar, Chintan and Quackenbush, John and Schwartz, Lawrence H. and Aerts, Hugo J. W. L.},
	month = aug,
	year = {2018},
	pmid = {29777175},
	pmcid = {PMC6268174},
	keywords = {Algorithms, Humans, Machine Learning, Pattern Recognition, Automated, Image Processing, Computer-Assisted, Radiology, Deep Learning, Artificial Intelligence, Neoplasms},
	pages = {500--510},
	file = {Accepted Version:C\:\\Users\\320253692\\Zotero\\storage\\SWLRLB5C\\Hosny et al. - 2018 - Artificial intelligence in radiology.pdf:application/pdf},
}

@article{najjar_redefining_2023,
	title = {Redefining {Radiology}: {A} {Review} of {Artificial} {Intelligence} {Integration} in {Medical} {Imaging}},
	volume = {13},
	issn = {2075-4418},
	shorttitle = {Redefining {Radiology}},
	abstract = {This comprehensive review unfolds a detailed narrative of Artificial Intelligence (AI) making its foray into radiology, a move that is catalysing transformational shifts in the healthcare landscape. It traces the evolution of radiology, from the initial discovery of X-rays to the application of machine learning and deep learning in modern medical image analysis. The primary focus of this review is to shed light on AI applications in radiology, elucidating their seminal roles in image segmentation, computer-aided diagnosis, predictive analytics, and workflow optimisation. A spotlight is cast on the profound impact of AI on diagnostic processes, personalised medicine, and clinical workflows, with empirical evidence derived from a series of case studies across multiple medical disciplines. However, the integration of AI in radiology is not devoid of challenges. The review ventures into the labyrinth of obstacles that are inherent to AI-driven radiology-data quality, the 'black box' enigma, infrastructural and technical complexities, as well as ethical implications. Peering into the future, the review contends that the road ahead for AI in radiology is paved with promising opportunities. It advocates for continuous research, embracing avant-garde imaging technologies, and fostering robust collaborations between radiologists and AI developers. The conclusion underlines the role of AI as a catalyst for change in radiology, a stance that is firmly rooted in sustained innovation, dynamic partnerships, and a steadfast commitment to ethical responsibility.},
	language = {eng},
	number = {17},
	journal = {Diagnostics (Basel, Switzerland)},
	author = {Najjar, Reabal},
	month = aug,
	year = {2023},
	pmid = {37685300},
	pmcid = {PMC10487271},
	keywords = {radiology, deep learning, machine learning, computer-aided diagnosis, medical imaging, artificial intelligence, convolutional neural networks, radiomics},
	pages = {2760},
	file = {Full Text:C\:\\Users\\320253692\\Zotero\\storage\\NJEH7APQ\\Najjar - 2023 - Redefining Radiology A Review of Artificial Intel.pdf:application/pdf},
}

@inproceedings{wang_review_2023,
	title = {A {Review} on {3D} {Convolutional} {Neural} {Network}},
	abstract = {The convolutional neural network (CNN) is a potent and popular neural network types and has been crucial to deep learning in recent years. A standard CNN which is known as 2-dimensions CNN was first proposed to solve image classification problems at first. With advancements in science and technology, as well as the growth of the internet, tasks involving video analysis have received increased attention in recent years. As a result, it was suggested that a 3-dimensions convolutional neural network (3D CNN) be applied to tackle 3D image processing or video analysis. 3D CNN is primarily used in the field of human activity recognition, video classification, medical imaging detection, and so on. This paper offers a succinct introduction to the general architecture and representative models of 3D CNN, contrasts the variations between 2D CNN and 3D CNN, explores the widely used models derived from 3D CNN, and displays the application outcomes of 3D CNN.},
	urldate = {2025-01-28},
	booktitle = {2023 {IEEE} 3rd {International} {Conference} on {Power}, {Electronics} and {Computer} {Applications} ({ICPECA})},
	author = {Wang, Chuqi},
	month = jan,
	year = {2023},
	keywords = {Convolutional neural networks, Neural networks, Convolutional Neural Network, 3D CNN, Analytical models, image classification, Power electronics, Solid modeling, Three-dimensional displays, Two dimensional displays, video analysis},
	pages = {1204--1208},
	file = {IEEE Xplore Abstract Record:C\:\\Users\\320253692\\Zotero\\storage\\26L5FIIE\\10075760.html:text/html},
}

@misc{singh_3d_2020,
	title = {{3D} {Deep} {Learning} on {Medical} {Images}: {A} {Review}},
	shorttitle = {{3D} {Deep} {Learning} on {Medical} {Images}},
	abstract = {The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for the analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, we provide a brief mathematical description of 3D CNN and provide the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field.},
	urldate = {2025-01-28},
	publisher = {arXiv},
	author = {Singh, Satya P. and Wang, Lipo and Gupta, Sukrit and Goli, Haveesh and Padmanabhan, Parasuraman and Gulyás, Balázs},
	month = oct,
	year = {2020},
	note = {arXiv:2004.00218 [q-bio]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods},
	annote = {Comment: Published in Sensors Journal (https://www.mdpi.com/1424-8220/20/18/5097)},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\SP355AHM\\Singh et al. - 2020 - 3D Deep Learning on Medical Images A Review.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\N448ST8C\\2004.html:text/html},
}

@inproceedings{oyedare_keep_2023,
	title = {Keep {It} {Simple}: {CNN} {Model} {Complexity} {Studies} for {Interference} {Classification} {Tasks}},
	shorttitle = {Keep {It} {Simple}},
	abstract = {The growing number of devices using the wireless spectrum makes it important to find ways to minimize interference and optimize the use of the spectrum. Deep learning models, such as convolutional neural networks (CNNs), have been widely utilized to identify, classify, or mitigate interference due to their ability to learn from the data directly. However, there have been limited research on the complexity of such deep learning models. The major focus of deep learning-based wireless classification literature has been on improving classification accuracy, often at the expense of model complexity. This may not be practical for many wireless devices, such as, internet of things (IoT) devices, which usually have very limited computational resources and cannot handle very complex models. Thus, it becomes important to account for model complexity when designing deep learning-based models for interference classification. To address this, we conduct an analysis of CNN based wireless classification that explores the trade-off amongst dataset size, CNN model complexity, and classification accuracy under various levels of classification difficulty: namely, interference classification, heterogeneous transmitter classification, and homogeneous transmitter classification. Our study, based on three wireless datasets, shows that a simpler CNN model with fewer parameters can perform just as well as a more complex model, providing important insights into the use of CNNs in computationally constrained applications.},
	urldate = {2025-01-28},
	booktitle = {{IEEE} {INFOCOM} 2023 - {IEEE} {Conference} on {Computer} {Communications} {Workshops} ({INFOCOM} {WKSHPS})},
	author = {Oyedare, Taiwo and Shah, Vijay K. and Jakubisin, Daniel J. and Reed, Jeffrey H.},
	month = may,
	year = {2023},
	note = {arXiv:2303.03326 [eess]},
	keywords = {Computer Science - Machine Learning, Computer Science - Networking and Internet Architecture, Electrical Engineering and Systems Science - Signal Processing},
	pages = {1--6},
	annote = {Comment: 6 pages, 7 figures, 3 tables},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\CUMBHUHC\\Oyedare et al. - 2023 - Keep It Simple CNN Model Complexity Studies for I.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\GYV3HC3A\\2303.html:text/html},
}

@article{halder_implementing_2024,
	title = {Implementing vision transformer for classifying {2D} biomedical images},
	volume = {14},
	copyright = {2024 The Author(s)},
	issn = {2045-2322},
	abstract = {In recent years, the growth spurt of medical imaging data has led to the development of various machine learning algorithms for various healthcare applications. The MedMNISTv2 dataset, a comprehensive benchmark for 2D biomedical image classification, encompasses diverse medical imaging modalities such as Fundus Camera, Breast Ultrasound, Colon Pathology, Blood Cell Microscope etc. Highly accurate classifications performed on these datasets is crucial for identification of various diseases and determining the course of treatment. This research paper presents a comprehensive analysis of four subsets within the MedMNISTv2 dataset: BloodMNIST, BreastMNIST, PathMNIST and RetinaMNIST. Each of these selected datasets is of diverse data modalities and comes with various sample sizes, and have been selected to analyze the efficiency of the model against diverse data modalities. The study explores the idea of assessing the Vision Transformer Model’s ability to capture intricate patterns and features crucial for these medical image classification and thereby transcend the benchmark metrics substantially. The methodology includes pre-processing the input images which is followed by training the ViT-base-patch16-224 model on the mentioned datasets. The performance of the model is assessed using key metrices and by comparing the classification accuracies achieved with the benchmark accuracies. With the assistance of ViT, the new benchmarks achieved for BloodMNIST, BreastMNIST, PathMNIST and RetinaMNIST are 97.90\%, 90.38\%, 94.62\% and 57\%, respectively. The study highlights the promise of Vision transformer models in medical image analysis, preparing the way for their adoption and further exploration in healthcare applications, aiming to enhance diagnostic accuracy and assist medical professionals in clinical decision-making.},
	language = {en},
	number = {1},
	urldate = {2025-01-28},
	journal = {Scientific Reports},
	author = {Halder, Arindam and Gharami, Sanghita and Sadhu, Priyangshu and Singh, Pawan Kumar and Woźniak, Marcin and Ijaz, Muhammad Fazal},
	month = may,
	year = {2024},
	note = {Publisher: Nature Publishing Group},
	keywords = {Computer science, Computational science, Information technology},
	pages = {12567},
	file = {Full Text PDF:C\:\\Users\\320253692\\Zotero\\storage\\PL6HAISW\\Halder et al. - 2024 - Implementing vision transformer for classifying 2D.pdf:application/pdf},
}

@article{azad_advances_2024,
	title = {Advances in medical image analysis with vision {Transformers}: {A} comprehensive review},
	volume = {91},
	issn = {1361-8415},
	shorttitle = {Advances in medical image analysis with vision {Transformers}},
	abstract = {The remarkable performance of the Transformer architecture in natural language processing has recently also triggered broad interest in Computer Vision. Among other merits, Transformers are witnessed as capable of learning long-range dependencies and spatial correlations, which is a clear advantage over convolutional neural networks (CNNs), which have been the de facto standard in Computer Vision problems so far. Thus, Transformers have become an integral part of modern medical image analysis. In this review, we provide an encyclopedic review of the applications of Transformers in medical imaging. Specifically, we present a systematic and thorough review of relevant recent Transformer literature for different medical image analysis tasks, including classification, segmentation, detection, registration, synthesis, and clinical report generation. For each of these applications, we investigate the novelty, strengths and weaknesses of the different proposed strategies and develop taxonomies highlighting key properties and contributions. Further, if applicable, we outline current benchmarks on different datasets. Finally, we summarize key challenges and discuss different future research directions. In addition, we have provided cited papers with their corresponding implementations in https://github.com/mindflow-institue/Awesome-Transformer.},
	urldate = {2025-01-28},
	journal = {Medical Image Analysis},
	author = {Azad, Reza and Kazerouni, Amirhossein and Heidari, Moein and Aghdam, Ehsan Khodapanah and Molaei, Amirali and Jia, Yiwei and Jose, Abin and Roy, Rijo and Merhof, Dorit},
	month = jan,
	year = {2024},
	keywords = {Deep neural networks, Transformers, Medical image analysis, Vision transformers},
	pages = {103000},
	file = {ScienceDirect Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\8G5X8GE3\\S1361841523002608.html:text/html;Submitted Version:C\:\\Users\\320253692\\Zotero\\storage\\2Z7LCJWZ\\Azad et al. - 2024 - Advances in medical image analysis with vision Tra.pdf:application/pdf},
}

@article{tucudean_natural_2024,
	title = {Natural language processing with transformers: a review},
	volume = {10},
	issn = {2376-5992},
	shorttitle = {Natural language processing with transformers},
	abstract = {Natural language processing (NLP) tasks can be addressed with several deep learning architectures, and many different approaches have proven to be efficient. This study aims to briefly summarize the use cases for NLP tasks along with the main architectures. This research presents transformer-based solutions for NLP tasks such as Bidirectional Encoder Representations from Transformers (BERT), and Generative Pre-Training (GPT) architectures. To achieve that, we conducted a step-by-step process in the review strategy: identify the recent studies that include Transformers, apply filters to extract the most consistent studies, identify and define inclusion and exclusion criteria, assess the strategy proposed in each study, and finally discuss the methods and architectures presented in the resulting articles. These steps facilitated the systematic summarization and comparative analysis of NLP applications based on Transformer architectures. The primary focus is the current state of the NLP domain, particularly regarding its applications, language models, and data set types. The results provide insights into the challenges encountered in this research domain.},
	language = {eng},
	journal = {PeerJ. Computer Science},
	author = {Tucudean, Georgiana and Bucos, Marian and Dragulescu, Bogdan and Caleanu, Catalin Daniel},
	year = {2024},
	pmid = {39145251},
	pmcid = {PMC11322986},
	keywords = {Natural language processing, Deep neural network architectures, Review, Transfomers, Trends},
	pages = {e2222},
}

@inproceedings{bardes_vicreg_2021,
	title = {{VICReg}: {Variance}-{Invariance}-{Covariance} {Regularization} for {Self}-{Supervised} {Learning}},
	shorttitle = {{VICReg}},
	abstract = {Recent self-supervised methods for image representation learning maximize the agreement between embedding vectors produced by encoders fed with different views of the same image. The main challenge is to prevent a collapse in which the encoders produce constant or non-informative vectors. We introduce VICReg (Variance-Invariance-Covariance Regularization), a method that explicitly avoids the collapse problem with two regularizations terms applied to both embeddings separately: (1) a term that maintains the variance of each embedding dimension above a threshold, (2) a term that decorrelates each pair of variables. Unlike most other approaches to the same problem, VICReg does not require techniques such as: weight sharing between the branches, batch normalization, feature-wise normalization, output quantization, stop gradient, memory banks, etc., and achieves results on par with the state of the art on several downstream tasks. In addition, we show that our variance regularization term stabilizes the training of other methods and leads to performance improvements.},
	language = {en},
	urldate = {2025-01-28},
	author = {Bardes, Adrien and Ponce, Jean and LeCun, Yann},
	month = oct,
	year = {2021},
	file = {Full Text PDF:C\:\\Users\\320253692\\Zotero\\storage\\AAQEWGR7\\Bardes et al. - 2021 - VICReg Variance-Invariance-Covariance Regularizat.pdf:application/pdf},
}

@article{ju_graph_2024,
	title = {Graph {Neural} {Network} {Model} for {Prediction} of {Non}-{Small} {Cell} {Lung} {Cancer} {Lymph} {Node} {Metastasis} {Using} {Protein}-{Protein} {Interaction} {Network} and {18F}-{FDG} {PET}/{CT} {Radiomics}},
	volume = {25},
	issn = {1422-0067},
	abstract = {The image texture features obtained from 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images of non-small cell lung cancer (NSCLC) have revealed tumor heterogeneity. A combination of genomic data and radiomics may improve the prediction of tumor prognosis. This study aimed to predict NSCLC metastasis using a graph neural network (GNN) obtained by combining a protein-protein interaction (PPI) network based on gene expression data and image texture features. 18F-FDG PET/CT images and RNA sequencing data of 93 patients with NSCLC were acquired from The Cancer Imaging Archive. Image texture features were extracted from 18F-FDG PET/CT images and area under the curve receiver operating characteristic curve (AUC) of each image feature was calculated. Weighted gene co-expression network analysis (WGCNA) was used to construct gene modules, followed by functional enrichment analysis and identification of differentially expressed genes. The PPI of each gene module and genes belonging to metastasis-related processes were converted via a graph attention network. Images and genomic features were concatenated. The GNN model using PPI modules from WGCNA and metastasis-related functions combined with image texture features was evaluated quantitatively. Fifty-five image texture features were extracted from 18F-FDG PET/CT, and radiomic features were selected based on AUC (n = 10). Eighty-six gene modules were clustered by WGCNA. Genes (n = 19) enriched in the metastasis-related pathways were filtered using DEG analysis. The accuracy of the PPI network, derived from WGCNA modules and metastasis-related genes, improved from 0.4795 to 0.5830 (p {\textless} 2.75 × 10-12). Integrating PPI of four metastasis-related genes with 18F-FDG PET/CT image features in a GNN model elevated its accuracy over a without image feature model to 0.8545 (95\% CI = 0.8401-0.8689, p-value {\textless} 0.02). This model demonstrated significant enhancement compared to the model using PPI and 18F-FDG PET/CT derived from WGCNA (p-value {\textless} 0.02), underscoring the critical role of metastasis-related genes in prediction model. The enhanced predictive capability of the lymph node metastasis prediction GNN model for NSCLC, achieved through the integration of comprehensive image features with genomic data, demonstrates promise for clinical implementation.},
	language = {eng},
	number = {2},
	journal = {International Journal of Molecular Sciences},
	author = {Ju, Hyemin and Kim, Kangsan and Kim, Byung Il and Woo, Sang-Keun},
	month = jan,
	year = {2024},
	pmid = {38255770},
	pmcid = {PMC10815846},
	keywords = {Humans, Neural Networks, Computer, Lung Neoplasms, Lymphatic Metastasis, 18F-FDG PET, Carcinoma, Non-Small-Cell Lung, CT, Fluorodeoxyglucose F18, GNN, NSCLC, Positron Emission Tomography Computed Tomography, Protein Interaction Maps, protein–protein interaction, radiogenomics, Radiomics},
	pages = {698},
	file = {Full Text:C\:\\Users\\320253692\\Zotero\\storage\\CQTA5RZX\\Ju et al. - 2024 - Graph Neural Network Model for Prediction of Non-S.pdf:application/pdf},
}

@article{jia_using_2024,
	title = {Using {3D} point cloud and graph-based neural networks to improve the estimation of pulmonary function tests from chest {CT}},
	volume = {182},
	issn = {0010-4825},
	abstract = {Pulmonary function tests (PFTs) are important clinical metrics to measure the severity of interstitial lung disease for systemic sclerosis patients. However, PFTs cannot always be performed by spirometry if there is a risk of disease transmission or other contraindications. In addition, it is unclear how lung function is affected by changes in lung vessels. Therefore, convolution neural networks (CNNs) were previously proposed to estimate PFTs from chest CT scans (CNN-CT) and extracted vessels (CNN-Vessel). Due to GPU memory constraints, however, these networks used down-sampled images, which causes a loss of information on small vessels. Previous literature has indicated that detailed vessel information from CT scans can be helpful for PFT estimation. Therefore, this paper proposes to use a point cloud neural network (PNN-Vessel) and graph neural network (GNN-Vessel) to estimate PFTs from point cloud and graph-based representations of pulmonary vessel centerlines, respectively. After that, we combine different networks and perform multiple variable step-wise regression analysis to explore if vessel-based networks can contribute to the PFT estimation, in addition to CNN-CT. Results showed that both PNN-Vessel and GNN-Vessel outperformed CNN-Vessel, by 14\% and 4\%, respectively, when averaged across the intra-class correlation coefficient (ICC) scores of four PFTs metrics. In addition, compared to CNN-Vessel, PNN-Vessel used 30\% of training time (1.1 h) and 7\% parameters (2.1 M) and GNN-Vessel used only 7\% training time (0.25 h) and 0.7\% parameters (0.2 M). We combined CNN-CT, PNN-Vessel and GNN-Vessel with the weights obtained from multiple variable regression methods, which achieved the best PFT estimation accuracy (ICC of 0.748, 0.742, 0.836 and 0.835 for the four PFT measures respectively). The results verified that more detailed vessel information could provide further explanation for PFT estimation from anatomical imaging.},
	urldate = {2025-01-28},
	journal = {Computers in Biology and Medicine},
	author = {Jia, Jingnan and Yu, Bo and Mody, Prerak and Ninaber, Maarten K. and Schouffoer, Anne A. and de Vries-Bouwstra, Jeska K. and Kroft, Lucia J. M. and Staring, Marius and Stoel, Berend C.},
	month = nov,
	year = {2024},
	keywords = {Deep learning, Computed tomography, Graph, Lung vessels, Point cloud, Pulmonary function test, Systemic sclerosis},
	pages = {109192},
	file = {ScienceDirect Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\8MMIA572\\S0010482524012770.html:text/html},
}

@book{goodfellow_deep_2016,
	title = {Deep {Learning}},
	urldate = {2025-01-28},
	publisher = {MIT Press},
	author = {Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron},
	year = {2016},
	file = {Deep Learning:C\:\\Users\\320253692\\Zotero\\storage\\AS92EMCD\\www.deeplearningbook.org.html:text/html},
}

@inproceedings{di_piazza_imitating_2025,
	title = {Imitating {Radiological} {Scrolling}: {A} {Global}-{Local} {Attention} {Model} for {3D} {Chest} {CT} {Volumes} {Multi}-{Label} {Anomaly} {Classification}},
	shorttitle = {Imitating {Radiological} {Scrolling}},
	abstract = {The rapid increase in the number of Computed Tomography (CT) scan examinations has created an urgent need for automated tools, such as organ segmentation, anomaly classification, and report generation, to assist radiologists with their growing workload. Multi-label classification of Three-Dimensional (3D) CT scans is a challenging task due to the volumetric nature of the data and the variety of anomalies to be detected. Existing deep learning classification methods, relying on standard Convolutional Neural Networks or Vision Transformers, do not explicitly model the radiologist's navigational behavior while scrolling through CT scan slices. In this study, we present CT-Scroll, a novel glocal-local attention model specifically designed to emulate the scrolling behavior of radiologists during the analysis of 3D CT scans. Our approach is evaluated on two public datasets, demonstrating its efficacy through comprehensive experiments and an ablation study that highlights the contribution of each model component.},
	language = {en},
	urldate = {2025-01-30},
	author = {Di Piazza, Théo and Lazarus, Carole and Nempont, Olivier and Boussel, Loic},
	month = jan,
	year = {2025},
	file = {Di Piazza et al. - 2025 - Imitating Radiological Scrolling A Glocal-Lobal A.pdf:C\:\\Users\\320253692\\Zotero\\storage\\9KXL8J4Y\\Di Piazza et al. - 2025 - Imitating Radiological Scrolling A Glocal-Lobal A.pdf:application/pdf;Piazza et al. - Imitating Radiological Scrolling A Global-Local A.pdf:C\:\\Users\\320253692\\Zotero\\storage\\EJ7F72L6\\Piazza et al. - Imitating Radiological Scrolling A Global-Local A.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\YCDWMSC9\\forum.html:text/html},
}

@misc{defferrard_convolutional_2017,
	title = {Convolutional {Neural} {Networks} on {Graphs} with {Fast} {Localized} {Spectral} {Filtering}},
	abstract = {In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words' embedding, represented by graphs. We present a formulation of CNNs in the context of spectral graph theory, which provides the necessary mathematical background and efficient numerical schemes to design fast localized convolutional filters on graphs. Importantly, the proposed technique offers the same linear computational complexity and constant learning complexity as classical CNNs, while being universal to any graph structure. Experiments on MNIST and 20NEWS demonstrate the ability of this novel deep learning system to learn local, stationary, and compositional features on graphs.},
	urldate = {2025-01-30},
	publisher = {arXiv},
	author = {Defferrard, Michaël and Bresson, Xavier and Vandergheynst, Pierre},
	month = feb,
	year = {2017},
	note = {arXiv:1606.09375 [cs]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	annote = {Comment: NIPS 2016 final revision},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\NU9Z3UHK\\Defferrard et al. - 2017 - Convolutional Neural Networks on Graphs with Fast .pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\EVHQHF4B\\1606.html:text/html},
}

@misc{kipf_semi-supervised_2017-1,
	title = {Semi-{Supervised} {Classification} with {Graph} {Convolutional} {Networks}},
	abstract = {We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin.},
	urldate = {2025-01-30},
	publisher = {arXiv},
	author = {Kipf, Thomas N. and Welling, Max},
	month = feb,
	year = {2017},
	note = {arXiv:1609.02907 [cs]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	annote = {Comment: Published as a conference paper at ICLR 2017},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\BSIH59GS\\Kipf and Welling - 2017 - Semi-Supervised Classification with Graph Convolut.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\QVYJD6YX\\1609.html:text/html},
}

@misc{wu_simplifying_2019,
	title = {Simplifying {Graph} {Convolutional} {Networks}},
	abstract = {Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations. GCNs derive inspiration primarily from recent deep learning approaches, and as a result, may inherit unnecessary complexity and redundant computation. In this paper, we reduce this excess complexity through successively removing nonlinearities and collapsing weight matrices between consecutive layers. We theoretically analyze the resulting linear model and show that it corresponds to a fixed low-pass filter followed by a linear classifier. Notably, our experimental evaluation demonstrates that these simplifications do not negatively impact accuracy in many downstream applications. Moreover, the resulting model scales to larger datasets, is naturally interpretable, and yields up to two orders of magnitude speedup over FastGCN.},
	urldate = {2025-01-30},
	publisher = {arXiv},
	author = {Wu, Felix and Zhang, Tianyi and Jr, Amauri Holanda de Souza and Fifty, Christopher and Yu, Tao and Weinberger, Kilian Q.},
	month = jun,
	year = {2019},
	note = {arXiv:1902.07153 [cs]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	annote = {Comment: In ICML 2019. Code available at https://github.com/Tiiiger/SGC},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\F99W9MHI\\Wu et al. - 2019 - Simplifying Graph Convolutional Networks.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\SZ96XR9Q\\1902.html:text/html},
}

@misc{velickovic_graph_2018,
	title = {Graph {Attention} {Networks}},
	abstract = {We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods' features, we enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront. In this way, we address several key challenges of spectral-based graph neural networks simultaneously, and make our model readily applicable to inductive as well as transductive problems. Our GAT models have achieved or matched state-of-the-art results across four established transductive and inductive graph benchmarks: the Cora, Citeseer and Pubmed citation network datasets, as well as a protein-protein interaction dataset (wherein test graphs remain unseen during training).},
	urldate = {2025-01-30},
	publisher = {arXiv},
	author = {Veličković, Petar and Cucurull, Guillem and Casanova, Arantxa and Romero, Adriana and Liò, Pietro and Bengio, Yoshua},
	month = feb,
	year = {2018},
	note = {arXiv:1710.10903 [stat]},
	keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning, Computer Science - Social and Information Networks},
	annote = {Comment: To appear at ICLR 2018. 12 pages, 2 figures},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\EAQ8Q6K8\\Veličković et al. - 2018 - Graph Attention Networks.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\WWIMBXEZ\\1710.html:text/html},
}

@misc{battaglia_interaction_2016,
	title = {Interaction {Networks} for {Learning} about {Objects}, {Relations} and {Physics}},
	abstract = {Reasoning about objects, relations, and physics is central to human intelligence, and a key goal of artificial intelligence. Here we introduce the interaction network, a model which can reason about how objects in complex systems interact, supporting dynamical predictions, as well as inferences about the abstract properties of the system. Our model takes graphs as input, performs object- and relation-centric reasoning in a way that is analogous to a simulation, and is implemented using deep neural networks. We evaluate its ability to reason about several challenging physical domains: n-body problems, rigid-body collision, and non-rigid dynamics. Our results show it can be trained to accurately simulate the physical trajectories of dozens of objects over thousands of time steps, estimate abstract quantities such as energy, and generalize automatically to systems with different numbers and configurations of objects and relations. Our interaction network implementation is the first general-purpose, learnable physics engine, and a powerful general framework for reasoning about object and relations in a wide variety of complex real-world domains.},
	urldate = {2025-01-30},
	publisher = {arXiv},
	author = {Battaglia, Peter W. and Pascanu, Razvan and Lai, Matthew and Rezende, Danilo and Kavukcuoglu, Koray},
	month = dec,
	year = {2016},
	note = {arXiv:1612.00222 [cs]},
	keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence},
	annote = {Comment: Published in NIPS 2016},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\KDUUMIBN\\Battaglia et al. - 2016 - Interaction Networks for Learning about Objects, R.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\KDWW4W4I\\1612.html:text/html},
}

@misc{gilmer_neural_2017,
	title = {Neural {Message} {Passing} for {Quantum} {Chemistry}},
	abstract = {Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science. Luckily, several promising and closely related neural network models invariant to molecular symmetries have already been described in the literature. These models learn a message passing algorithm and aggregation procedure to compute a function of their entire input graph. At this point, the next step is to find a particularly effective variant of this general approach and apply it to chemical prediction benchmarks until we either solve them or reach the limits of the approach. In this paper, we reformulate existing models into a single common framework we call Message Passing Neural Networks (MPNNs) and explore additional novel variations within this framework. Using MPNNs we demonstrate state of the art results on an important molecular property prediction benchmark; these results are strong enough that we believe future work should focus on datasets with larger molecules or more accurate ground truth labels.},
	urldate = {2025-01-30},
	publisher = {arXiv},
	author = {Gilmer, Justin and Schoenholz, Samuel S. and Riley, Patrick F. and Vinyals, Oriol and Dahl, George E.},
	month = jun,
	year = {2017},
	note = {arXiv:1704.01212 [cs]},
	keywords = {Computer Science - Machine Learning},
	annote = {Comment: 14 pages},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\J8LXGZTI\\Gilmer et al. - 2017 - Neural Message Passing for Quantum Chemistry.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\7U78LWCW\\1704.html:text/html},
}

@misc{battaglia_relational_2018,
	title = {Relational inductive biases, deep learning, and graph networks},
	abstract = {Artificial intelligence (AI) has undergone a renaissance recently, making major progress in key domains such as vision, language, control, and decision-making. This has been due, in part, to cheap data and cheap compute resources, which have fit the natural strengths of deep learning. However, many defining characteristics of human intelligence, which developed under much different pressures, remain out of reach for current approaches. In particular, generalizing beyond one's experiences--a hallmark of human intelligence from infancy--remains a formidable challenge for modern AI. The following is part position paper, part review, and part unification. We argue that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective. Just as biology uses nature and nurture cooperatively, we reject the false choice between "hand-engineering" and "end-to-end" learning, and instead advocate for an approach which benefits from their complementary strengths. We explore how using relational inductive biases within deep learning architectures can facilitate learning about entities, relations, and rules for composing them. We present a new building block for the AI toolkit with a strong relational inductive bias--the graph network--which generalizes and extends various approaches for neural networks that operate on graphs, and provides a straightforward interface for manipulating structured knowledge and producing structured behaviors. We discuss how graph networks can support relational reasoning and combinatorial generalization, laying the foundation for more sophisticated, interpretable, and flexible patterns of reasoning. As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice.},
	urldate = {2025-01-30},
	publisher = {arXiv},
	author = {Battaglia, Peter W. and Hamrick, Jessica B. and Bapst, Victor and Sanchez-Gonzalez, Alvaro and Zambaldi, Vinicius and Malinowski, Mateusz and Tacchetti, Andrea and Raposo, David and Santoro, Adam and Faulkner, Ryan and Gulcehre, Caglar and Song, Francis and Ballard, Andrew and Gilmer, Justin and Dahl, George and Vaswani, Ashish and Allen, Kelsey and Nash, Charles and Langston, Victoria and Dyer, Chris and Heess, Nicolas and Wierstra, Daan and Kohli, Pushmeet and Botvinick, Matt and Vinyals, Oriol and Li, Yujia and Pascanu, Razvan},
	month = oct,
	year = {2018},
	note = {arXiv:1806.01261 [cs]},
	keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\HMYAY6IK\\Battaglia et al. - 2018 - Relational inductive biases, deep learning, and gr.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\DDW3WAER\\1806.html:text/html},
}

@incollection{ross_paired_2017,
	address = {Rotterdam},
	title = {Paired {Samples} {T}-{Test}},
	isbn = {978-94-6351-086-8},
	abstract = {A paired-samples t-test compares the mean of two matched groups of people or cases, or compares the mean of a single group, examined at two different points in time. If the same group is tested again, on the same measure, the t-test is called a repeated measures t-test.},
	language = {en},
	urldate = {2025-02-01},
	booktitle = {Basic and {Advanced} {Statistical} {Tests}: {Writing} {Results} {Sections} and {Creating} {Tables} and {Figures}},
	publisher = {SensePublishers},
	author = {Ross, Amanda and Willson, Victor L.},
	editor = {Ross, Amanda and Willson, Victor L.},
	year = {2017},
	doi = {10.1007/978-94-6351-086-8_4},
	pages = {17--19},
}

@misc{xu_how_2019,
	title = {How {Powerful} are {Graph} {Neural} {Networks}?},
	abstract = {Graph Neural Networks (GNNs) are an effective framework for representation learning of graphs. GNNs follow a neighborhood aggregation scheme, where the representation vector of a node is computed by recursively aggregating and transforming representation vectors of its neighboring nodes. Many GNN variants have been proposed and have achieved state-of-the-art results on both node and graph classification tasks. However, despite GNNs revolutionizing graph representation learning, there is limited understanding of their representational properties and limitations. Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures. Our results characterize the discriminative power of popular GNN variants, such as Graph Convolutional Networks and GraphSAGE, and show that they cannot learn to distinguish certain simple graph structures. We then develop a simple architecture that is provably the most expressive among the class of GNNs and is as powerful as the Weisfeiler-Lehman graph isomorphism test. We empirically validate our theoretical findings on a number of graph classification benchmarks, and demonstrate that our model achieves state-of-the-art performance.},
	urldate = {2025-02-03},
	publisher = {arXiv},
	author = {Xu, Keyulu and Hu, Weihua and Leskovec, Jure and Jegelka, Stefanie},
	month = feb,
	year = {2019},
	note = {arXiv:1810.00826 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\CFUPLK4M\\Xu et al. - 2019 - How Powerful are Graph Neural Networks.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\ACJN2UCM\\1810.html:text/html},
}

@article{ji_3d_2013,
	title = {{3D} {Convolutional} {Neural} {Networks} for {Human} {Action} {Recognition}},
	volume = {35},
	issn = {1939-3539},
	abstract = {We consider the automated recognition of human actions in surveillance videos. Most current methods build classifiers based on complex handcrafted features computed from the raw inputs. Convolutional neural networks (CNNs) are a type of deep model that can act directly on the raw inputs. However, such models are currently limited to handling 2D inputs. In this paper, we develop a novel 3D CNN model for action recognition. This model extracts features from both the spatial and the temporal dimensions by performing 3D convolutions, thereby capturing the motion information encoded in multiple adjacent frames. The developed model generates multiple channels of information from the input frames, and the final feature representation combines information from all channels. To further boost the performance, we propose regularizing the outputs with high-level features and combining the predictions of a variety of different models. We apply the developed models to recognize human actions in the real-world environment of airport surveillance videos, and they achieve superior performance in comparison to baseline methods.},
	number = {1},
	urldate = {2025-02-03},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	author = {Ji, Shuiwang and Xu, Wei and Yang, Ming and Yu, Kai},
	month = jan,
	year = {2013},
	note = {Conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence},
	keywords = {Deep learning, Feature extraction, Computational modeling, convolutional neural networks, Solid modeling, 3D convolution, action recognition, Computer architecture, Kernel, model combination, Three dimensional displays, Videos},
	pages = {221--231},
	file = {IEEE Xplore Abstract Record:C\:\\Users\\320253692\\Zotero\\storage\\LFXJFG7U\\6165309.html:text/html},
}

@misc{cicek_3d_2016,
	title = {{3D} {U}-{Net}: {Learning} {Dense} {Volumetric} {Segmentation} from {Sparse} {Annotation}},
	shorttitle = {{3D} {U}-{Net}},
	abstract = {This paper introduces a network for volumetric segmentation that learns from sparsely annotated volumetric images. We outline two attractive use cases of this method: (1) In a semi-automated setup, the user annotates some slices in the volume to be segmented. The network learns from these sparse annotations and provides a dense 3D segmentation. (2) In a fully-automated setup, we assume that a representative, sparsely annotated training set exists. Trained on this data set, the network densely segments new volumetric images. The proposed network extends the previous u-net architecture from Ronneberger et al. by replacing all 2D operations with their 3D counterparts. The implementation performs on-the-fly elastic deformations for efficient data augmentation during training. It is trained end-to-end from scratch, i.e., no pre-trained network is required. We test the performance of the proposed method on a complex, highly variable 3D structure, the Xenopus kidney, and achieve good results for both use cases.},
	urldate = {2025-02-03},
	publisher = {arXiv},
	author = {Çiçek, Özgün and Abdulkadir, Ahmed and Lienkamp, Soeren S. and Brox, Thomas and Ronneberger, Olaf},
	month = jun,
	year = {2016},
	note = {arXiv:1606.06650 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	annote = {Comment: Conditionally accepted for MICCAI 2016},
	file = {Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\786CEMMN\\1606.html:text/html},
}

@misc{soltani_hyperedge_2024,
	title = {Hyperedge {Modeling} in {Hypergraph} {Neural} {Networks} by using {Densest} {Overlapping} {Subgraphs}},
	abstract = {Hypergraphs tackle the limitations of traditional graphs by introducing \{{\textbackslash}em hyperedges\}. While graph edges connect only two nodes, hyperedges connect an arbitrary number of nodes along their edges. Also, the underlying message-passing mechanisms in Hypergraph Neural Networks (HGNNs) are in the form of vertex-hyperedge-vertex, which let HGNNs capture and utilize richer and more complex structural information than traditional Graph Neural Networks (GNNs). More recently, the idea of overlapping subgraphs has emerged. These subgraphs can capture more information about subgroups of vertices without limiting one vertex belonging to just one group, allowing vertices to belong to multiple groups or subgraphs. In addition, one of the most important problems in graph clustering is to find densest overlapping subgraphs (DOS). In this paper, we propose a solution to the DOS problem via Agglomerative Greedy Enumeration (DOSAGE) algorithm as a novel approach to enhance the process of generating the densest overlapping subgraphs and, hence, a robust construction of the hypergraphs. Experiments on standard benchmarks show that the DOSAGE algorithm significantly outperforms the HGNNs and six other methods on the node classification task.},
	urldate = {2025-02-03},
	publisher = {arXiv},
	author = {Soltani, Mehrad and Rueda, Luis},
	month = sep,
	year = {2024},
	note = {arXiv:2409.10340 [cs]},
	keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Social and Information Networks},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\XBD4QKTF\\Soltani and Rueda - 2024 - Hyperedge Modeling in Hypergraph Neural Networks b.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\HEPNPBR2\\2409.html:text/html},
}

@misc{guo_higt_2023,
	title = {{HIGT}: {Hierarchical} {Interaction} {Graph}-{Transformer} for {Whole} {Slide} {Image} {Analysis}},
	shorttitle = {{HIGT}},
	abstract = {In computation pathology, the pyramid structure of gigapixel Whole Slide Images (WSIs) has recently been studied for capturing various information from individual cell interactions to tissue microenvironments. This hierarchical structure is believed to be beneficial for cancer diagnosis and prognosis tasks. However, most previous hierarchical WSI analysis works (1) only characterize local or global correlations within the WSI pyramids and (2) use only unidirectional interaction between different resolutions, leading to an incomplete picture of WSI pyramids. To this end, this paper presents a novel Hierarchical Interaction Graph-Transformer (i.e., HIGT) for WSI analysis. With Graph Neural Network and Transformer as the building commons, HIGT can learn both short-range local information and long-range global representation of the WSI pyramids. Considering that the information from different resolutions is complementary and can benefit each other during the learning process, we further design a novel Bidirectional Interaction block to establish communication between different levels within the WSI pyramids. Finally, we aggregate both coarse-grained and fine-grained features learned from different levels together for slide-level prediction. We evaluate our methods on two public WSI datasets from TCGA projects, i.e., kidney carcinoma (KICA) and esophageal carcinoma (ESCA). Experimental results show that our HIGT outperforms both hierarchical and non-hierarchical state-of-the-art methods on both tumor subtyping and staging tasks.},
	urldate = {2025-02-21},
	publisher = {arXiv},
	author = {Guo, Ziyu and Zhao, Weiqin and Wang, Shujun and Yu, Lequan},
	month = sep,
	year = {2023},
	note = {arXiv:2309.07400 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	annote = {Comment: Accepted by MICCAI2023; Code is available in https://github.com/HKU-MedAI/HIGT},
	file = {MICCAI_HierarchicalGraph.pdf:C\:\\Users\\320253692\\Zotero\\storage\\NRP98F9R\\MICCAI_HierarchicalGraph.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\LM7Q2PKZ\\2309.html:text/html},
}

@misc{bruna_spectral_2014,
	title = {Spectral {Networks} and {Locally} {Connected} {Networks} on {Graphs}},
	abstract = {Convolutional Neural Networks are extremely efficient architectures in image and audio recognition tasks, thanks to their ability to exploit the local translational invariance of signal classes over their domain. In this paper we consider possible generalizations of CNNs to signals defined on more general domains without the action of a translation group. In particular, we propose two constructions, one based upon a hierarchical clustering of the domain, and another based on the spectrum of the graph Laplacian. We show through experiments that for low-dimensional graphs it is possible to learn convolutional layers with a number of parameters independent of the input size, resulting in efficient deep architectures.},
	urldate = {2025-02-22},
	publisher = {arXiv},
	author = {Bruna, Joan and Zaremba, Wojciech and Szlam, Arthur and LeCun, Yann},
	month = may,
	year = {2014},
	note = {arXiv:1312.6203 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing},
	annote = {Comment: 14 pages},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\BGZYW498\\Bruna et al. - 2014 - Spectral Networks and Locally Connected Networks o.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\2NWQGBT3\\1312.html:text/html},
}

@inproceedings{belkin_laplacian_2001,
	title = {Laplacian {Eigenmaps} and {Spectral} {Techniques} for {Embedding} and {Clustering}},
	volume = {14},
	abstract = {Drawing  on  the  correspondence  between  the graph  Laplacian,  the  Laplace-Beltrami  operator  on  a  manifold , and  the  connections  to  the heat equation , we propose a geometrically motivated algorithm  for  constructing  a  representation  for  data sampled from  a  low  di(cid:173) mensional manifold embedded  in  a  higher  dimensional space.  The  algorithm  provides  a  computationally  efficient  approach  to  non(cid:173) linear  dimensionality  reduction  that  has  locality  preserving  prop(cid:173) erties  and  a  natural  connection  to  clustering.  Several  applications  are  considered.},
	urldate = {2025-02-22},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher = {MIT Press},
	author = {Belkin, Mikhail and Niyogi, Partha},
	year = {2001},
	file = {Full Text PDF:C\:\\Users\\320253692\\Zotero\\storage\\UQ8URPYB\\Belkin and Niyogi - 2001 - Laplacian Eigenmaps and Spectral Techniques for Em.pdf:application/pdf},
}

@misc{ma_u-mamba_2024,
	title = {U-{Mamba}: {Enhancing} {Long}-range {Dependency} for {Biomedical} {Image} {Segmentation}},
	shorttitle = {U-{Mamba}},
	abstract = {Convolutional Neural Networks (CNNs) and Transformers have been the most popular architectures for biomedical image segmentation, but both of them have limited ability to handle long-range dependencies because of inherent locality or computational complexity. To address this challenge, we introduce U-Mamba, a general-purpose network for biomedical image segmentation. Inspired by the State Space Sequence Models (SSMs), a new family of deep sequence models known for their strong capability in handling long sequences, we design a hybrid CNN-SSM block that integrates the local feature extraction power of convolutional layers with the abilities of SSMs for capturing the long-range dependency. Moreover, U-Mamba enjoys a self-configuring mechanism, allowing it to automatically adapt to various datasets without manual intervention. We conduct extensive experiments on four diverse tasks, including the 3D abdominal organ segmentation in CT and MR images, instrument segmentation in endoscopy images, and cell segmentation in microscopy images. The results reveal that U-Mamba outperforms state-of-the-art CNN-based and Transformer-based segmentation networks across all tasks. This opens new avenues for efficient long-range dependency modeling in biomedical image analysis. The code, models, and data are publicly available at https://wanglab.ai/u-mamba.html.},
	urldate = {2025-02-25},
	publisher = {arXiv},
	author = {Ma, Jun and Li, Feifei and Wang, Bo},
	month = jan,
	year = {2024},
	note = {arXiv:2401.04722 [eess]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Machine Learning},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\YYSZBGXN\\Ma et al. - 2024 - U-Mamba Enhancing Long-range Dependency for Biome.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\UYWLJG4H\\2401.html:text/html},
}

@misc{zhu_hhgt_2024,
	title = {{HHGT}: {Hierarchical} {Heterogeneous} {Graph} {Transformer} for {Heterogeneous} {Graph} {Representation} {Learning}},
	shorttitle = {{HHGT}},
	abstract = {Despite the success of Heterogeneous Graph Neural Networks (HGNNs) in modeling real-world Heterogeneous Information Networks (HINs), challenges such as expressiveness limitations and over-smoothing have prompted researchers to explore Graph Transformers (GTs) for enhanced HIN representation learning. However, research on GT in HINs remains limited, with two key shortcomings in existing work: (1) A node's neighbors at different distances in HINs convey diverse semantics. Unfortunately, existing methods ignore such differences and uniformly treat neighbors within a given distance in a coarse manner, which results in semantic confusion. (2) Nodes in HINs have various types, each with unique semantics. Nevertheless, existing methods mix nodes of different types during neighbor aggregation, hindering the capture of proper correlations between nodes of diverse types. To bridge these gaps, we design an innovative structure named (k,t)-ring neighborhood, where nodes are initially organized by their distance, forming different non-overlapping k-ring neighborhoods for each distance. Within each k-ring structure, nodes are further categorized into different groups according to their types, thus emphasizing the heterogeneity of both distances and types in HINs naturally. Based on this structure, we propose a novel Hierarchical Heterogeneous Graph Transformer (HHGT) model, which seamlessly integrates a Type-level Transformer for aggregating nodes of different types within each k-ring neighborhood, followed by a Ring-level Transformer for aggregating different k-ring neighborhoods in a hierarchical manner. Extensive experiments are conducted on downstream tasks to verify HHGT's superiority over 14 baselines, with a notable improvement of up to 24.75\% in NMI and 29.25\% in ARI for node clustering task on the ACM dataset compared to the best baseline.},
	urldate = {2025-03-28},
	publisher = {arXiv},
	author = {Zhu, Qiuyu and Zhang, Liang and Xu, Qianxiong and Liu, Kaijun and Long, Cheng and Wang, Xiaoyang},
	month = jul,
	year = {2024},
	note = {arXiv:2407.13158 [cs]},
	keywords = {Computer Science - Machine Learning, Computer Science - Databases},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\7PQ3E2IE\\Zhu et al. - 2024 - HHGT Hierarchical Heterogeneous Graph Transformer.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\KWVZJFG4\\2407.html:text/html},
}

@misc{wang_hierarchical_2021,
	title = {Hierarchical {Heterogeneous} {Graph} {Representation} {Learning} for {Short} {Text} {Classification}},
	abstract = {Short text classification is a fundamental task in natural language processing. It is hard due to the lack of context information and labeled data in practice. In this paper, we propose a new method called SHINE, which is based on graph neural network (GNN), for short text classification. First, we model the short text dataset as a hierarchical heterogeneous graph consisting of word-level component graphs which introduce more semantic and syntactic information. Then, we dynamically learn a short document graph that facilitates effective label propagation among similar short texts. Thus, compared with existing GNN-based methods, SHINE can better exploit interactions between nodes of the same types and capture similarities between short texts. Extensive experiments on various benchmark short text datasets show that SHINE consistently outperforms state-of-the-art methods, especially with fewer labels.},
	urldate = {2025-03-28},
	publisher = {arXiv},
	author = {Wang, Yaqing and Wang, Song and Yao, Quanming and Dou, Dejing},
	month = oct,
	year = {2021},
	note = {arXiv:2111.00180 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Artificial Intelligence},
	annote = {Comment: Accepted to EMNLP 2021},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\WVDQ9RAE\\Wang et al. - 2021 - Hierarchical Heterogeneous Graph Representation Le.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\JTNY7ZQA\\2111.html:text/html},
}

@misc{zhang_adapting_2023,
	title = {Adapting {Pre}-trained {Vision} {Transformers} from {2D} to {3D} through {Weight} {Inflation} {Improves} {Medical} {Image} {Segmentation}},
	abstract = {Given the prevalence of 3D medical imaging technologies such as MRI and CT that are widely used in diagnosing and treating diverse diseases, 3D segmentation is one of the fundamental tasks of medical image analysis. Recently, Transformer-based models have started to achieve state-of-the-art performances across many vision tasks, through pre-training on large-scale natural image benchmark datasets. While works on medical image analysis have also begun to explore Transformer-based models, there is currently no optimal strategy to effectively leverage pre-trained Transformers, primarily due to the difference in dimensionality between 2D natural images and 3D medical images. Existing solutions either split 3D images into 2D slices and predict each slice independently, thereby losing crucial depth-wise information, or modify the Transformer architecture to support 3D inputs without leveraging pre-trained weights. In this work, we use a simple yet effective weight inflation strategy to adapt pre-trained Transformers from 2D to 3D, retaining the benefit of both transfer learning and depth information. We further investigate the effectiveness of transfer from different pre-training sources and objectives. Our approach achieves state-of-the-art performances across a broad range of 3D medical image datasets, and can become a standard strategy easily utilized by all work on Transformer-based models for 3D medical images, to maximize performance.},
	urldate = {2025-05-24},
	publisher = {arXiv},
	author = {Zhang, Yuhui and Huang, Shih-Cheng and Zhou, Zhengping and Lungren, Matthew P. and Yeung, Serena},
	month = feb,
	year = {2023},
	note = {arXiv:2302.04303 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	annote = {Comment: Published at ML4H 2022},
	file = {Full Text PDF:C\:\\Users\\320253692\\Zotero\\storage\\V92D9C6K\\Zhang et al. - 2023 - Adapting Pre-trained Vision Transformers from 2D t.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\VA87A7UJ\\2302.html:text/html},
}

@inproceedings{arnab_vivit_2021,
	address = {Montreal, QC, Canada},
	title = {{ViViT}: {A} {Video} {Vision} {Transformer}},
	copyright = {https://doi.org/10.15223/policy-029},
	isbn = {978-1-66542-812-5},
	shorttitle = {{ViViT}},
	abstract = {We present pure-transformer based models for video classiﬁcation, drawing upon the recent success of such models in image classiﬁcation. Our model extracts spatiotemporal tokens from the input video, which are then encoded by a series of transformer layers. In order to handle the long sequences of tokens encountered in video, we propose several, efﬁcient variants of our model which factorise the spatial- and temporal-dimensions of the input. Although transformer-based models are known to only be effective when large training datasets are available, we show how we can effectively regularise the model during training and leverage pretrained image models to be able to train on comparatively small datasets. We conduct thorough ablation studies, and achieve state-of-the-art results on multiple video classiﬁcation benchmarks including Kinetics 400 and 600, Epic Kitchens, Something-Something v2 and Moments in Time, outperforming prior methods based on deep 3D convolutional networks.},
	language = {en},
	urldate = {2025-05-27},
	booktitle = {2021 {IEEE}/{CVF} {International} {Conference} on {Computer} {Vision} ({ICCV})},
	publisher = {IEEE},
	author = {Arnab, Anurag and Dehghani, Mostafa and Heigold, Georg and Sun, Chen and Lucic, Mario and Schmid, Cordelia},
	month = oct,
	year = {2021},
	pages = {6816--6826},
	file = {Arnab et al. - 2021 - ViViT A Video Vision Transformer.pdf:C\:\\Users\\320253692\\Zotero\\storage\\LH2FWLT3\\Arnab et al. - 2021 - ViViT A Video Vision Transformer.pdf:application/pdf},
}

@misc{kiechle_graph_2024,
	title = {Graph {Neural} {Networks}: {A} suitable {Alternative} to {MLPs} in {Latent} {3D} {Medical} {Image} {Classification}?},
	shorttitle = {Graph {Neural} {Networks}},
	abstract = {Recent studies have underscored the capabilities of natural imaging foundation models to serve as powerful feature extractors, even in a zero-shot setting for medical imaging data. Most commonly, a shallow multi-layer perceptron (MLP) is appended to the feature extractor to facilitate end-to-end learning and downstream prediction tasks such as classification, thus representing the de facto standard. However, as graph neural networks (GNNs) have become a practicable choice for various tasks in medical research in the recent past, we direct attention to the question of how effective GNNs are compared to MLP prediction heads for the task of 3D medical image classification, proposing them as a potential alternative. In our experiments, we devise a subject-level graph for each volumetric dataset instance. Therein latent representations of all slices in the volume, encoded through a DINOv2 pretrained vision transformer (ViT), constitute the nodes and their respective node features. We use public datasets to compare the classification heads numerically and evaluate various graph construction and graph convolution methods in our experiments. Our findings show enhancements of the GNN in classification performance and substantial improvements in runtime compared to an MLP prediction head. Additional robustness evaluations further validate the promising performance of the GNN, promoting them as a suitable alternative to traditional MLP classification heads. Our code is publicly available at: https://github.com/compai-lab/2024-miccai-grail-kiechle},
	urldate = {2025-07-11},
	publisher = {arXiv},
	author = {Kiechle, Johannes and Lang, Daniel M. and Fischer, Stefan M. and Felsner, Lina and Peeken, Jan C. and Schnabel, Julia A.},
	month = jul,
	year = {2024},
	note = {arXiv:2407.17219 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	annote = {Comment: Accepted at MICCAI 2024 - GRAIL Workshop},
	file = {Preprint PDF:C\:\\Users\\320253692\\Zotero\\storage\\JLGBU4G9\\Kiechle et al. - 2024 - Graph Neural Networks A suitable Alternative to M.pdf:application/pdf;Snapshot:C\:\\Users\\320253692\\Zotero\\storage\\S4U5WULX\\2407.html:text/html},
}
