@article{nguyen_lvm-med_nodate,
	title = {{LVM}-{Med}: {Learning} {Large}-{Scale} {Self}-{Supervised} {Vision} {Models} for {Medical} {Imaging} via {Second}-order {Graph} {Matching}},
	abstract = {Obtaining large pre-trained models that can be fine-tuned to new tasks with limited annotated samples has remained an open challenge for medical imaging data. While pre-trained deep networks on ImageNet and vision-language foundation models trained on web-scale data are prevailing approaches, their effectiveness on medical tasks is limited due to the significant domain shift between natural and medical images. To bridge this gap, we introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million in medical images from 55 publicly available datasets, covering a large number of organs and modalities such as CT, MRI, X-ray, and Ultrasound. We benchmark several state-of-the-art self-supervised algorithms on this dataset and propose a novel self-supervised contrastive learning algorithm using a graph matching formulation. The proposed approach makes three contributions: (i) it integrates prior pair-wise image similarity metrics based on local and global information; (ii) it captures the structural constraints of feature embeddings through a loss function constructed via a combinatorial graph-matching objective; and (iii) it can be trained efficiently end-to-end using modern gradient-estimation techniques for black-box solvers. We thoroughly evaluate the proposed LVM-Med on 15 downstream medical tasks ranging from segmentation and classification to object detection, and both for the in and out-of-distribution settings. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models. For challenging tasks such as Brain Tumor Classification or Diabetic Retinopathy Grading, LVM-Med improves previous vision-language models trained on 1 billion masks by 6-7\% while using only a ResNet-50. We release pre-trained models at this link https://github.com/duyhominhnguyen/LVM-Med.},
	language = {en},
	author = {Nguyen, Duy M H and Nguyen, Hoang and Diep, Nghiem T and Pham, Tan N and Cao, Tri and Nguyen, Binh T and others},
}

@incollection{linguraru_diffrect_2024,
	address = {Cham},
	title = {{DiffRect}: {Latent} {Diffusion} {Label} {Rectification} for {Semi}-supervised {Medical} {Image} {Segmentation}},
	volume = {15012},
	isbn = {978-3-031-72389-6 978-3-031-72390-2},
	shorttitle = {{DiffRect}},
	url = {https://link.springer.com/10.1007/978-3-031-72390-2_6},
	abstract = {Semi-supervised medical image segmentation aims to leverage limited annotated data and rich unlabeled data to perform accurate segmentation. However, existing semi-supervised methods are highly dependent on the quality of self-generated pseudo labels, which are prone to incorrect supervision and confirmation bias. Meanwhile, they are insufficient in capturing the label distributions in latent space and suffer from limited generalization to unlabeled data. To address these issues, we propose a Latent Diffusion Label Rectification Model (DiffRect) for semi-supervised medical image segmentation. DiffRect first utilizes a Label Context Calibration Module (LCC) to calibrate the biased relationship between classes by learning the category-wise correlation in pseudo labels, then apply Latent Feature Rectification Module (LFR) on the latent space to formulate and align the pseudo label distributions of different levels via latent diffusion. It utilizes a denoising network to learn the coarse to fine and fine to precise consecutive distribution transportations. We evaluate DiffRect on three public datasets: ACDC, MS-CMRSEG 2019, and Decathlon Prostate. Experimental results demonstrate the effectiveness of DiffRect, e.g. it achieves 82.40\% Dice score on ACDC with only 1\% labeled scan available, outperforms the previous state-of-the-art by 4.60\% in Dice, and even rivals fully supervised performance. Code is released at https://github.com/CUHK-AIM-Group/DiffRect.},
	language = {en},
	urldate = {2025-05-02},
	booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2024},
	publisher = {Springer Nature Switzerland},
	author = {Liu, Xinyu and Li, Wuyang and Yuan, Yixuan},
	editor = {Linguraru, Marius George and Dou, Qi and Feragen, Aasa and Giannarou, Stamatia and Glocker, Ben and Lekadir, Karim and Schnabel, Julia A.},
	year = {2024},
	doi = {10.1007/978-3-031-72390-2_6},
	note = {Series Title: Lecture Notes in Computer Science},
	pages = {56--66},
}

@misc{meissen_robust_2023,
	title = {Robust {Detection} {Outcome}: {A} {Metric} for {Pathology} {Detection} in {Medical} {Images}},
	shorttitle = {Robust {Detection} {Outcome}},
	url = {http://arxiv.org/abs/2303.01920},
	doi = {10.48550/arXiv.2303.01920},
	abstract = {Detection of pathologies is a fundamental task in medical imaging and the evaluation of algorithms that can perform this task automatically is crucial. However, current object detection metrics for natural images do not reflect the specific clinical requirements in pathology detection sufficiently. To tackle this problem, we propose Robust Detection Outcome (RoDeO); a novel metric for evaluating algorithms for pathology detection in medical images, especially in chest X-rays. RoDeO evaluates different errors directly and individually, and reflects clinical needs better than current metrics. Extensive evaluation on the ChestX-ray8 dataset shows the superiority of our metrics compared to existing ones. We released the code at https://github.com/FeliMe/RoDeO and published RoDeO as pip package (rodeometric).},
	urldate = {2025-02-14},
	publisher = {arXiv},
	author = {Meissen, Felix and Müller, Philip and Kaissis, Georgios and Rueckert, Daniel},
	month = mar,
	year = {2023},
	note = {arXiv:2303.01920 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@misc{noauthor_google-bertbert-base-uncased_2024,
	title = {google-bert/bert-base-uncased · {Hugging} {Face}},
	url = {https://huggingface.co/google-bert/bert-base-uncased},
	abstract = {We’re on a journey to advance and democratize artificial intelligence through open source and open science.},
	urldate = {2025-02-14},
	month = mar,
	year = {2024},
}

@article{lu_multi-window_2020,
	title = {Multi-{Window} {CT} {Based} {Radiological} {Traits} for {Improving} {Early} {Detection} in {Lung} {Cancer} {Screening}},
	volume = {12},
	issn = {1179-1322},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7707434/},
	doi = {10.2147/CMAR.S246609},
	abstract = {Rationale and Objectives
Evaluate ability of radiological semantic traits assessed on multi-window computed tomography (CT) to predict lung cancer risk.

Materials and Methods
A total of 199 participants were investigated, including 60 incident lung cancers and 139 benign positive controls. Twenty lung window features and 2 mediastinal window features were extracted and scored on a point scale in three screening rounds. Multivariate logistic regression analysis was used to explore the association of these radiological traits with the risk of developing lung cancer. The areas under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and positive predictive value (PPV) were computed to evaluate the best predictive model.

Results
Combining mediastinal window-specific features with the lung window features-based model significantly improves performance compared to individual window features. Model performance is consistent both at baseline and the first follow-up scan, with an AUROC increased from 0.822 to 0.871 (p = 0.009) and from 0.877 to 0.917 (p = 0.008), respectively, for single to multi-window feature models. We also find that the multi-window CT based model showed better specificity and PPV, with PPV at the second follow-up scan improved to 0.953.

Conclusion
We find combining window semantic features improves model performance in identifying cancerous nodules. We also find that lung window features are more informative compared to mediastinal features in predicting malignancy.},
	urldate = {2025-02-04},
	journal = {Cancer Management and Research},
	author = {Lu, Hong and Kim, Jongphil and Qi, Jin and Li, Qian and Liu, Ying and Schabath, Matthew B and others},
	month = nov,
	year = {2020},
	pmid = {33273859},
	pmcid = {PMC7707434},
	pages = {12225--12238},
}

@article{isensee_nnu-net_2021,
	title = {{nnU}-{Net}: a self-configuring method for deep learning-based biomedical image segmentation},
	volume = {18},
	copyright = {2020 The Author(s), under exclusive licence to Springer Nature America, Inc.},
	issn = {1548-7105},
	shorttitle = {{nnU}-{Net}},
	url = {https://www.nature.com/articles/s41592-020-01008-z},
	doi = {10.1038/s41592-020-01008-z},
	abstract = {Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation algorithms enable image analysis and quantification in many applications, the design of respective specialized solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We developed nnU-Net, a deep learning-based segmentation method that automatically configures itself, including preprocessing, network architecture, training and post-processing for any new task. The key design choices in this process are modeled as a set of fixed parameters, interdependent rules and empirical decisions. Without manual intervention, nnU-Net surpasses most existing approaches, including highly specialized solutions on 23 public datasets used in international biomedical segmentation competitions. We make nnU-Net publicly available as an out-of-the-box tool, rendering state-of-the-art segmentation accessible to a broad audience by requiring neither expert knowledge nor computing resources beyond standard network training.},
	language = {en},
	number = {2},
	urldate = {2025-01-26},
	journal = {Nature Methods},
	author = {Isensee, Fabian and Jaeger, Paul F. and Kohl, Simon A. A. and Petersen, Jens and Maier-Hein, Klaus H.},
	month = feb,
	year = {2021},
	note = {Publisher: Nature Publishing Group},
	keywords = {Image processing, Translational research},
	pages = {203--211},
}

@misc{wang_medclip_2022,
	title = {{MedCLIP}: {Contrastive} {Learning} from {Unpaired} {Medical} {Images} and {Text}},
	shorttitle = {{MedCLIP}},
	url = {http://arxiv.org/abs/2210.10163},
	doi = {10.48550/arXiv.2210.10163},
	abstract = {Existing vision-text contrastive learning like CLIP aims to match the paired image and caption embeddings while pushing others apart, which improves representation transferability and supports zero-shot prediction. However, medical image-text datasets are orders of magnitude below the general images and captions from the internet. Moreover, previous methods encounter many false negatives, i.e., images and reports from separate patients probably carry the same semantics but are wrongly treated as negatives. In this paper, we decouple images and texts for multimodal contrastive learning thus scaling the usable training data in a combinatorial magnitude with low cost. We also propose to replace the InfoNCE loss with semantic matching loss based on medical knowledge to eliminate false negatives in contrastive learning. We prove that MedCLIP is a simple yet effective framework: it outperforms state-of-the-art methods on zero-shot prediction, supervised classification, and image-text retrieval. Surprisingly, we observe that with only 20K pre-training data, MedCLIP wins over the state-of-the-art method (using around 200K data). Our code is available at https://github.com/RyanWangZf/MedCLIP.},
	urldate = {2025-01-05},
	publisher = {arXiv},
	author = {Wang, Zifeng and Wu, Zhenbang and Agarwal, Dinesh and Sun, Jimeng},
	month = oct,
	year = {2022},
	note = {arXiv:2210.10163 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition},
}

@misc{marimont_achieving_2023,
	title = {Achieving state-of-the-art performance in the {Medical} {Out}-of-{Distribution} ({MOOD}) challenge using plausible synthetic anomalies},
	url = {http://arxiv.org/abs/2308.01412},
	doi = {10.48550/arXiv.2308.01412},
	abstract = {The detection and localization of anomalies is one important medical image analysis task. Most commonly, Computer Vision anomaly detection approaches rely on manual annotations that are both time consuming and expensive to obtain. Unsupervised anomaly detection, or Out-of-Distribution detection, aims at identifying anomalous samples relying only on unannotated samples considered normal. In this study we present a new unsupervised anomaly detection method. Our method builds upon the self-supervised strategy consisting on training a segmentation network to identify local synthetic anomalies. Our contributions improve the synthetic anomaly generation process, making synthetic anomalies more heterogeneous and challenging by 1) using complex random shapes and 2) smoothing the edges of synthetic anomalies so networks cannot rely on the high gradient between image and synthetic anomalies. In our implementation we adopted standard practices in 3D medical image segmentation, including 3D U-Net architecture, patch-wise training and model ensembling. Our method was evaluated using a validation set with different types of synthetic anomalies. Our experiments show that our method improved substantially the baseline method performance. Additionally, we evaluated our method by participating in the Medical Out-of-Distribution (MOOD) Challenge held at MICCAI in 2022 and achieved first position in both sample-wise and pixel-wise tasks. Our experiments and results in the latest MOOD challenge show that our simple yet effective approach can substantially improve the performance of Out-of-Distribution detection techniques which rely on synthetic anomalies.},
	urldate = {2025-01-05},
	publisher = {arXiv},
	author = {Marimont, Sergio Naval and Tarroni, Giacomo},
	month = nov,
	year = {2023},
	note = {arXiv:2308.01412 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@book{segre_e_experimental_1953,
	title = {Experimental {Nuclear} {Physics} {Vol} 1},
	url = {http://archive.org/details/dli.ernet.242816},
	abstract = {Source: Digital Library of India

Scanning Centre: Allama Iqbal Library, University of Kashmir
Source Library: Allama Iqbal Library University Of Kashmir
Date Accessioned: 7/15/2015 20:21
The Digital Library of India was a project under the auspices of the Government of India.},
	language = {eng},
	urldate = {2024-12-05},
	publisher = {John Wiley and Sons Inc New York Chapman and Hall, Ltd London},
	author = {Segre E, Ed},
	year = {1953},
	keywords = {Natural Sciences},
}

@article{neloy_comprehensive_2024,
	title = {A comprehensive study of auto-encoders for anomaly detection: {Efficiency} and trade-offs},
	volume = {17},
	issn = {2666-8270},
	shorttitle = {A comprehensive study of auto-encoders for anomaly detection},
	url = {https://www.sciencedirect.com/science/article/pii/S2666827024000483},
	doi = {10.1016/j.mlwa.2024.100572},
	abstract = {Unsupervised anomaly detection (UAD) is a diverse research area explored across various application domains. Over time, numerous anomaly detection techniques, including clustering, generative, and variational inference-based methods, are developed to address specific drawbacks and advance state-of-the-art techniques. Deep learning and generative models recently played a significant role in identifying unique challenges and devising advanced approaches. Auto-encoders (AEs) represent one such powerful technique that combines generative and probabilistic variational modeling with deep architecture. Auto-Encoder aims to learn the underlying data distribution to generate consequential sample data. This concept of data generation and the adoption of generative modeling have emerged in extensive research and variations in Auto-Encoder design, particularly in unsupervised representation learning. This study systematically reviews 11 Auto-Encoder architectures categorized into three groups, aiming to differentiate their reconstruction ability, sample generation, latent space visualization, and accuracy in classifying anomalous data using the Fashion-MNIST (FMNIST) and MNIST datasets. Additionally, we closely observed the reproducibility scope under different training parameters. We conducted reproducibility experiments utilizing similar model setups and hyperparameters and attempted to generate comparative results to address the scope of improvements for each Auto-Encoder. We conclude this study by analyzing the experimental results, which guide us in identifying the efficiency and trade-offs among auto-encoders, providing valuable insights into their performance and applicability in unsupervised anomaly detection techniques.},
	urldate = {2024-12-04},
	journal = {Machine Learning with Applications},
	author = {Neloy, Asif Ahmed and Turgeon, Maxime},
	month = sep,
	year = {2024},
	keywords = {Anomaly detection, Auto-encoders, Deep learning, Reproducibility},
	pages = {100572},
}

@inproceedings{tschuchnig_anomaly_2022,
	address = {Wiesbaden},
	title = {Anomaly {Detection} in {Medical} {Imaging} - {A} {Mini} {Review}},
	isbn = {978-3-658-36295-9},
	doi = {10.1007/978-3-658-36295-9_5},
	abstract = {The increasing digitization of medical imaging enables machine learning based improvements in detecting, visualizing and segmenting lesions, easing the workload for medical experts. However, supervised machine learning requires reliable labelled data, which is is often difficult or impossible to collect or at least time consuming and thereby costly. Therefore methods requiring only partly labeled data (semi-supervised) or no labeling at all (unsupervised methods) have been applied more regularly. Anomaly detection is one possible methodology that is able to leverage semi-supervised and unsupervised methods to handle medical imaging tasks like classification and segmentation. This paper uses a semi-exhaustive literature review of relevant anomaly detection papers in medical imaging to cluster into applications, highlight important results, establish lessons learned and give further advice on how to approach anomaly detection in medical imaging. The qualitative analysis is based on google scholar and 4 different search terms, resulting in 120 different analysed papers. The main results showed that the current research is mostly motivated by reducing the need for labelled data. Also, the successful and substantial amount of research in the brain MRI domain shows the potential for applications in further domains like OCT and chest X-ray.},
	language = {en},
	booktitle = {Data {Science} – {Analytics} and {Applications}},
	publisher = {Springer Fachmedien},
	author = {Tschuchnig, Maximilian E. and Gadermayr, Michael},
	editor = {Haber, Peter and Lampoltshammer, Thomas J. and Leopold, Helmut and Mayr, Manfred},
	year = {2022},
	pages = {33--38},
}

@inproceedings{bercea_mask_2023,
	title = {Mask, {Stitch}, and {Re}-{Sample}: {Enhancing} {Robustness} and {Generalizability} in {Anomaly} {Detection} through {Automatic} {Diffusion} {Models}},
	shorttitle = {Mask, {Stitch}, and {Re}-{Sample}},
	url = {https://openreview.net/forum?id=kTpafpXrqa#all},
	abstract = {The introduction of diffusion models in anomaly detection has paved the way for more effective and accurate pseudo-healthy synthesis. However , the current limitations in controlling noise granularity hinder the ability of diffusion models to generalize across diverse anomaly types and compromise the restoration of healthy tissues. To overcome these challenges, we propose AutoDDPM, a novel approach that enhances the robustness of diffusion models. AutoDDPM utilizes diffusion models to generate initial likelihood maps of potential anomalies and seamlessly integrates healthy tissues in the de-noising process. By re-sampling from the joint noised distribution, AutoDDPM achieves harmonization and in-painting effects. Our study demonstrates the efficacy of AutoDDPM in replacing anomalous regions while preserving healthy tissues, considerably surpassing diffusion models’ limitations. It also contributes valuable insights and analysis on the limitations of current diffusion models, promoting robust and interpretable anomaly detection in medical imaging — an essential aspect of building autonomous clinical decision systems with higher interpretability. Code: https://github.com/ci-ber/autoDDPM},
	language = {en},
	urldate = {2024-12-03},
	author = {Bercea, Cosmin I. and Neumayr, Michael and Rueckert, Daniel and Schnabel, Julia A.},
	month = jul,
	year = {2023},
}

@misc{bercea_denoising_2024,
	title = {Denoising {Diffusion} {Models} for {Anomaly} {Localization} in {Medical} {Images}},
	url = {http://arxiv.org/abs/2410.23834},
	doi = {10.48550/arXiv.2410.23834},
	abstract = {This chapter explores anomaly localization in medical images using denoising diffusion models. After providing a brief methodological background of these models, including their application to image reconstruction and their conditioning using guidance mechanisms, we provide an overview of available datasets and evaluation metrics suitable for their application to anomaly localization in medical images. In this context, we discuss supervision schemes ranging from fully supervised segmentation to semi-supervised, weakly supervised, self-supervised, and unsupervised methods, and provide insights into the effectiveness and limitations of these approaches. Furthermore, we highlight open challenges in anomaly localization, including detection bias, domain shift, computational cost, and model interpretability. Our goal is to provide an overview of the current state of the art in the field, outline research gaps, and highlight the potential of diffusion models for robust anomaly localization in medical images.},
	urldate = {2024-12-03},
	publisher = {arXiv},
	author = {Bercea, Cosmin I. and Cattin, Philippe C. and Schnabel, Julia A. and Wolleb, Julia},
	month = oct,
	year = {2024},
	note = {arXiv:2410.23834},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing},
}

@misc{themes_x-ray_2020,
	title = {X-{Ray} {Imaging}: {Fundamentals}},
	shorttitle = {X-{Ray} {Imaging}},
	url = {https://radiologykey.com/x-ray-imaging-fundamentals/},
	abstract = {X-Ray Imaging: Fundamentals Michael Burch, MD Eric England, MD Sammy Abid Yacob, DO Kaushal Mehta, MD LEARNING OBJECTIVES 1. Name the four major types of interactions for X-ray photons. 2. Define h…},
	language = {en-US},
	urldate = {2024-12-02},
	journal = {Radiology Key},
	author = {Themes, U. F. O.},
	month = apr,
	year = {2020},
}

@book{beutel_handbook_2000,
	title = {Handbook of {Medical} {Imaging}},
	isbn = {978-0-8194-3621-4},
	abstract = {A little more than 100 years after the discovery of x-rays, this three-volume set is intended to provide a comprehensive overview of the theory and current practice of Medical Imaging as we enter the 21st century. As evidenced by the variety of research described in these volumes, medical imaging is still undergoing very rapid change. In more than 50 chapters, well-known experts provide the most current information available for students, researchers and practitioners working in this exciting field. These useful volumes can be ordered as a set or individually.},
	language = {en},
	publisher = {SPIE Press},
	author = {Beutel, Jacob and Kundel, Harold L. and Metter, Richard L. Van},
	year = {2000},
	note = {Google-Books-ID: HQ2qiJd6FgEC},
	keywords = {Medical / Allied Health Services / Imaging Technologies, Medical / Diagnostic Imaging / General, Psychology / Physiological Psychology},
}

@misc{noauthor_ct_2017,
	title = {{CT} equipment - {Radiology} {Cafe}},
	howpublished = {\url{https://www.radiologycafe.com/frcr-physics-notes/ct-imaging/ct-equipment/}},
	abstract = {FRCR Physics Notes: Components of a CT scanner, filter, collimator, detector arrays, gantrys, 1st-4th generations of CT scanner and electron beam scanners.},
	language = {en-GB},
	urldate = {2024-12-02},
	month = mar,
	year = {2017},
    note = {Accessed: 2024-12-02}
}

@misc{urban_crowds_2019,
	title = {Crowds {Cure} {Cancer}: {Crowdsourced} data collected at the {RSNA} 2018 annual meeting},
	copyright = {TCIA Limited Access License},
	shorttitle = {Crowds {Cure} {Cancer}},
	url = {https://www.cancerimagingarchive.net/analysis-result/crowds-cure-2018/},
	doi = {10.7937/TCIA.2019.YK0GM1EB},
	abstract = {The Cancer Imaging Archive (TCIA) has procured substantial troves of image data, which could serve as valuable training sets for improving machine learning algorithms. However, these datasets lack consistent lesion annotations. To address this issue, the Cancer Imaging Informatics Lab at the Frederick National Laboratory for Cancer Research (FNLCR) formed a partnership with five groups funded by the National Cancer Institute's Informatics Technology for Cancer Research program to develop a web-based crowdsourcing application for gathering lesion annotations, featured at the Radiological Society of North America (RNSA) 2018 annual meeting.Crowds Cure Cancer (https://www.crowds-cure.org) first exhibited at RSNA 2017 utilizing CT scans from 4 different TCIA collections. Participants were asked to make a uni-dimensional measurement of the largest lesion. There were no options to provide details regarding imaging quality (e.g., no IV contrast, motion artifact, etc.), lesion location (e.g., lung, liver, etc.) or lesion characteristics (e.g., ill-defined, ground glass, etc.), requiring additional post-collection image review. The 2017 dataset can be found at https://doi.org/10.7937/K9/TCIA.2018.OW73VLO2 .For RSNA 2018, the application was re-designed to promote more comprehensive data collection and increase community participation. Participants were instructed to identify all metastatic disease and provide details regarding image quality, lesion location and characteristics. To provide additional incentives for participation, we improved the system by adding gamification features (e.g., reward badges), and created a leaderboard to display participant standings. The amount of data being annotated was also significantly increased to include CT scans from 13 TCIA collections: Anti-PD-1\_Lung, Anti-PD-1\_MELANOMA, CPTAC-CCRCC, CPTAC-GBM, CPTAC-HNSCC, CPTAC-PDA, CPTAC-UCEC, NSCLC Radiogenomics, TCGA-BLCA, TCGA-COAD, TCGA-HNSC, TCGA-LUSC, TCGA-UCEC.During RSNA 2018, 4756 bi-directional measurements were obtained compared to 2345 uni-dimensional measurements in 2017. Of the 4756 measurements, 65 of the lesions were annotated with location information. The data is being released in DICOM Structured Report and CSV formats for analysis by the community. The application is available on GitHub https://github.com/crowds-cure/cancer .},
	urldate = {2024-12-02},
	publisher = {The Cancer Imaging Archive},
	author = {Urban, Trinity and Ziegler, Erik and Pieper, Steve and Kirby, Justin and Rukas, Daniel and Beardmore, Britney and others},
	collaborator = {{TCIA Team}},
	year = {2019},
}

@article{yan_deeplesion_2018,
	title = {{DeepLesion}: automated mining of large-scale lesion annotations and universal lesion detection with deep learning},
	volume = {5},
	issn = {2329-4302},
	shorttitle = {{DeepLesion}},
	doi = {10.1117/1.JMI.5.3.036501},
	abstract = {Extracting, harvesting, and building large-scale annotated radiological image datasets is a greatly important yet challenging problem. Meanwhile, vast amounts of clinical annotations have been collected and stored in hospitals' picture archiving and communication systems (PACS). These types of annotations, also known as bookmarks in PACS, are usually marked by radiologists during their daily workflow to highlight significant image findings that may serve as reference for later studies. We propose to mine and harvest these abundant retrospective medical data to build a large-scale lesion image dataset. Our process is scalable and requires minimum manual annotation effort. We mine bookmarks in our institute to develop DeepLesion, a dataset with 32,735 lesions in 32,120 CT slices from 10,594 studies of 4,427 unique patients. There are a variety of lesion types in this dataset, such as lung nodules, liver tumors, enlarged lymph nodes, and so on. It has the potential to be used in various medical image applications. Using DeepLesion, we train a universal lesion detector that can find all types of lesions with one unified framework. In this challenging task, the proposed lesion detector achieves a sensitivity of 81.1\% with five false positives per image.},
	language = {eng},
	number = {3},
	journal = {Journal of Medical Imaging (Bellingham, Wash.)},
	author = {Yan, Ke and Wang, Xiaosong and Lu, Le and Summers, Ronald M.},
	month = jul,
	year = {2018},
	pmid = {30035154},
	pmcid = {PMC6052252},
	keywords = {bookmark, convolutional neural network, deep learning, lesion detection, medical image dataset, picture archiving and communication system},
	pages = {036501},
}

@inproceedings{roth_new_2014,
	address = {Cham},
	title = {A {New} 2.{5D} {Representation} for {Lymph} {Node} {Detection} {Using} {Random} {Sets} of {Deep} {Convolutional} {Neural} {Network} {Observations}},
	isbn = {978-3-319-10404-1},
	doi = {10.1007/978-3-319-10404-1_65},
	abstract = {Automated Lymph Node (LN) detection is an important clinical diagnostic task but very challenging due to the low contrast of surrounding structures in Computed Tomography (CT) and to their varying sizes, poses, shapes and sparsely distributed locations. State-of-the-art studies show the performance range of 52.9\% sensitivity at 3.1 false-positives per volume (FP/vol.), or 60.9\% at 6.1 FP/vol. for mediastinal LN, by one-shot boosting on 3D HAAR features. In this paper, we first operate a preliminary candidate generation stage, towards {\textasciitilde}100\% sensitivity at the cost of high FP levels ({\textasciitilde}40 per patient), to harvest volumes of interest (VOI). Our 2.5D approach consequently decomposes any 3D VOI by resampling 2D reformatted orthogonal views N times, via scale, random translations, and rotations with respect to the VOI centroid coordinates. These random views are then used to train a deep Convolutional Neural Network (CNN) classifier. In testing, the CNN is employed to assign LN probabilities for all N random views that can be simply averaged (as a set) to compute the final classification probability per VOI. We validate the approach on two datasets: 90 CT volumes with 388 mediastinal LNs and 86 patients with 595 abdominal LNs. We achieve sensitivities of 70\%/83\% at 3 FP/vol. and 84\%/90\% at 6 FP/vol. in mediastinum and abdomen respectively, which drastically improves over the previous state-of-the-art work.},
	language = {en},
	booktitle = {Medical {Image} {Computing} and {Computer}-{Assisted} {Intervention} – {MICCAI} 2014},
	publisher = {Springer International Publishing},
	author = {Roth, Holger R. and Lu, Le and Seff, Ari and Cherry, Kevin M. and Hoffman, Joanne and Wang, Shijun and others},
	editor = {Golland, Polina and Hata, Nobuhiko and Barillot, Christian and Hornegger, Joachim and Howe, Robert},
	year = {2014},
	keywords = {Convolutional Neural Network, Convolutional Neural Network Model, Deep Convolutional Neural Network, Image Patch, Lymph Node Candidate},
	pages = {520--527},
}

@article{antonelli_medical_2022,
	title = {The {Medical} {Segmentation} {Decathlon}},
	volume = {13},
	issn = {2041-1723},
	doi = {10.1038/s41467-022-30695-9},
	abstract = {International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)-a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training.},
	language = {eng},
	number = {1},
	journal = {Nature Communications},
	author = {Antonelli, Michela and Reinke, Annika and Bakas, Spyridon and Farahani, Keyvan and Kopp-Schneider, Annette and Landman, Bennett A. and others},
	month = jul,
	year = {2022},
	pmid = {35840566},
	pmcid = {PMC9287542},
	keywords = {Algorithms, Image Processing, Computer-Assisted},
	pages = {4128},
}

@article{bilic_liver_2023,
	title = {The {Liver} {Tumor} {Segmentation} {Benchmark} ({LiTS})},
	volume = {84},
	issn = {1361-8415},
	url = {https://www.sciencedirect.com/science/article/pii/S1361841522003085},
	doi = {10.1016/j.media.2022.102680},
	abstract = {In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094.},
	urldate = {2024-12-02},
	journal = {Medical Image Analysis},
	author = {Bilic, Patrick and Christ, Patrick and Li, Hongwei Bran and Vorontsov, Eugene and Ben-Cohen, Avi and Kaissis, Georgios and others},
	month = feb,
	year = {2023},
	keywords = {Benchmark, CT, Deep learning, Liver, Liver tumor, Segmentation},
	pages = {102680},
}

@article{armato_lung_2011,
	title = {The {Lung} {Image} {Database} {Consortium} ({LIDC}) and {Image} {Database} {Resource} {Initiative} ({IDRI}): a completed reference database of lung nodules on {CT} scans},
	volume = {38},
	issn = {0094-2405},
	shorttitle = {The {Lung} {Image} {Database} {Consortium} ({LIDC}) and {Image} {Database} {Resource} {Initiative} ({IDRI})},
	doi = {10.1118/1.3528204},
	abstract = {PURPOSE: The development of computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process.
METHODS: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ("nodule {\textgreater} or =3 mm," "nodule {\textless}3 mm," and "non-nodule {\textgreater} or =3 mm"). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus.
RESULTS: The Database contains 7371 lesions marked "nodule" by at least one radiologist. 2669 of these lesions were marked "nodule {\textgreater} or =3 mm" by at least one radiologist, of which 928 (34.7\%) received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings.
CONCLUSIONS: The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.},
	language = {eng},
	number = {2},
	journal = {Medical Physics},
	author = {Armato, Samuel G. and McLennan, Geoffrey and Bidaut, Luc and McNitt-Gray, Michael F. and Meyer, Charles R. and Reeves, Anthony P. and others},
	month = feb,
	year = {2011},
	pmid = {21452728},
	pmcid = {PMC3041807},
	keywords = {Databases, Factual, Diagnosis, Computer-Assisted, Humans, Lung, Lung Neoplasms, Quality Control, Radiographic Image Interpretation, Computer-Assisted, Radiography, Thoracic, Reference Standards, Tomography, X-Ray Computed, Tumor Burden},
	pages = {915--931},
}

@misc{heller_kits21_2023,
	title = {The {KiTS21} {Challenge}: {Automatic} segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase {CT}},
	shorttitle = {The {KiTS21} {Challenge}},
	url = {http://arxiv.org/abs/2307.01984},
	doi = {10.48550/arXiv.2307.01984},
	abstract = {This paper presents the challenge report for the 2021 Kidney and Kidney Tumor Segmentation Challenge (KiTS21) held in conjunction with the 2021 international conference on Medical Image Computing and Computer Assisted Interventions (MICCAI). KiTS21 is a sequel to its first edition in 2019, and it features a variety of innovations in how the challenge was designed, in addition to a larger dataset. A novel annotation method was used to collect three separate annotations for each region of interest, and these annotations were performed in a fully transparent setting using a web-based annotation tool. Further, the KiTS21 test set was collected from an outside institution, challenging participants to develop methods that generalize well to new populations. Nonetheless, the top-performing teams achieved a significant improvement over the state of the art set in 2019, and this performance is shown to inch ever closer to human-level performance. An in-depth meta-analysis is presented describing which methods were used and how they faired on the leaderboard, as well as the characteristics of which cases generally saw good performance, and which did not. Overall KiTS21 facilitated a significant advancement in the state of the art in kidney tumor segmentation, and provides useful insights that are applicable to the field of semantic segmentation as a whole.},
	urldate = {2024-12-02},
	publisher = {arXiv},
	author = {Heller, Nicholas and Isensee, Fabian and Trofimova, Dasha and Tejpaul, Resha and Zhao, Zhongchen and Chen, Huai and others},
	month = jul,
	year = {2023},
	note = {arXiv:2307.01984},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@article{rother_grabcut_2004,
	title = {"{GrabCut}": interactive foreground extraction using iterated graph cuts},
	volume = {23},
	issn = {0730-0301},
	shorttitle = {"{GrabCut}"},
	url = {https://dl.acm.org/doi/10.1145/1015706.1015720},
	doi = {10.1145/1015706.1015720},
	abstract = {The problem of efficient, interactive foreground/background segmentation in still images is of great practical importance in image editing. Classical image segmentation tools use either texture (colour) information, e.g. Magic Wand, or edge (contrast) information, e.g. Intelligent Scissors. Recently, an approach based on optimization by graph-cut has been developed which successfully combines both types of information. In this paper we extend the graph-cut approach in three respects. First, we have developed a more powerful, iterative version of the optimisation. Secondly, the power of the iterative algorithm is used to simplify substantially the user interaction needed for a given quality of result. Thirdly, a robust algorithm for "border matting" has been developed to estimate simultaneously the alpha-matte around an object boundary and the colours of foreground pixels. We show that for moderately difficult examples the proposed method outperforms competitive tools.},
	number = {3},
	urldate = {2024-11-29},
	journal = {ACM Trans. Graph.},
	author = {Rother, Carsten and Kolmogorov, Vladimir and Blake, Andrew},
	month = aug,
	year = {2004},
	pages = {309--314},
}

@misc{noauthor_sulis_nodate,
	title = {Sulis : {A} {Tier} 2 {HPC} {Platform} for {Ensemble} {Computing}},
	url = {https://warwick.ac.uk/research/rtp/sc/sulis/},
	urldate = {2024-11-25},
}

@inproceedings{lin_microsoft_2014,
	address = {Cham},
	title = {Microsoft {COCO}: {Common} {Objects} in {Context}},
	isbn = {978-3-319-10602-1},
	shorttitle = {Microsoft {COCO}},
	doi = {10.1007/978-3-319-10602-1_48},
	abstract = {We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.},
	language = {en},
	booktitle = {Computer {Vision} – {ECCV} 2014},
	publisher = {Springer International Publishing},
	author = {Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and others},
	editor = {Fleet, David and Pajdla, Tomas and Schiele, Bernt and Tuytelaars, Tinne},
	year = {2014},
	keywords = {Common Object, Object Category, Object Detection, Object Instance, Scene Understanding},
	pages = {740--755},
}

@misc{li_grounded_2022,
	title = {Grounded {Language}-{Image} {Pre}-training},
	url = {http://arxiv.org/abs/2112.03857},
	doi = {10.48550/arXiv.2112.03857},
	abstract = {This paper presents a grounded language-image pre-training (GLIP) model for learning object-level, language-aware, and semantic-rich visual representations. GLIP unifies object detection and phrase grounding for pre-training. The unification brings two benefits: 1) it allows GLIP to learn from both detection and grounding data to improve both tasks and bootstrap a good grounding model; 2) GLIP can leverage massive image-text pairs by generating grounding boxes in a self-training fashion, making the learned representation semantic-rich. In our experiments, we pre-train GLIP on 27M grounding data, including 3M human-annotated and 24M web-crawled image-text pairs. The learned representations demonstrate strong zero-shot and few-shot transferability to various object-level recognition tasks. 1) When directly evaluated on COCO and LVIS (without seeing any images in COCO during pre-training), GLIP achieves 49.8 AP and 26.9 AP, respectively, surpassing many supervised baselines. 2) After fine-tuned on COCO, GLIP achieves 60.8 AP on val and 61.5 AP on test-dev, surpassing prior SoTA. 3) When transferred to 13 downstream object detection tasks, a 1-shot GLIP rivals with a fully-supervised Dynamic Head. Code is released at https://github.com/microsoft/GLIP.},
	urldate = {2024-11-25},
	publisher = {arXiv},
	author = {Li, Liunian Harold and Zhang, Pengchuan and Zhang, Haotian and Yang, Jianwei and Li, Chunyuan and Zhong, Yiwu and others},
	month = jun,
	year = {2022},
	note = {arXiv:2112.03857},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Multimedia},
}

@misc{zhang_dino_2022,
	title = {{DINO}: {DETR} with {Improved} {DeNoising} {Anchor} {Boxes} for {End}-to-{End} {Object} {Detection}},
	shorttitle = {{DINO}},
	url = {http://arxiv.org/abs/2203.03605},
	doi = {10.48550/arXiv.2203.03605},
	abstract = {We present DINO ({\textbackslash}textbf\{D\}ETR with {\textbackslash}textbf\{I\}mproved de{\textbackslash}textbf\{N\}oising anch{\textbackslash}textbf\{O\}r boxes), a state-of-the-art end-to-end object detector. \% in this paper. DINO improves over previous DETR-like models in performance and efficiency by using a contrastive way for denoising training, a mixed query selection method for anchor initialization, and a look forward twice scheme for box prediction. DINO achieves \$49.4\$AP in \$12\$ epochs and \$51.3\$AP in \$24\$ epochs on COCO with a ResNet-50 backbone and multi-scale features, yielding a significant improvement of \${\textbackslash}textbf\{+6.0\}\${\textbackslash}textbf\{AP\} and \${\textbackslash}textbf\{+2.7\}\${\textbackslash}textbf\{AP\}, respectively, compared to DN-DETR, the previous best DETR-like model. DINO scales well in both model size and data size. Without bells and whistles, after pre-training on the Objects365 dataset with a SwinL backbone, DINO obtains the best results on both COCO {\textbackslash}texttt\{val2017\} (\${\textbackslash}textbf\{63.2\}\${\textbackslash}textbf\{AP\}) and {\textbackslash}texttt\{test-dev\} ({\textbackslash}textbf\{\${\textbackslash}textbf\{63.3\}\$AP\}). Compared to other models on the leaderboard, DINO significantly reduces its model size and pre-training data size while achieving better results. Our code will be available at {\textbackslash}url\{https://github.com/IDEACVR/DINO\}.},
	urldate = {2024-11-25},
	publisher = {arXiv},
	author = {Zhang, Hao and Li, Feng and Liu, Shilong and Zhang, Lei and Su, Hang and Zhu, Jun and others},
	month = jul,
	year = {2022},
	note = {arXiv:2203.03605},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@misc{lang_3d_2023,
	title = {{3D} {Masked} {Autoencoders} with {Application} to {Anomaly} {Detection} in {Non}-{Contrast} {Enhanced} {Breast} {MRI}},
	url = {http://arxiv.org/abs/2303.05861},
	doi = {10.48550/arXiv.2303.05861},
	abstract = {Self-supervised models allow (pre-)training on unlabeled data and therefore have the potential to overcome the need for large annotated cohorts. One leading self-supervised model is the masked autoencoder (MAE) which was developed on natural imaging data. The MAE is masking out a high fraction of visual transformer (ViT) input patches, to then recover the uncorrupted images as a pretraining task. In this work, we extend MAE to perform anomaly detection on breast magnetic resonance imaging (MRI). This new model, coined masked autoencoder for medical imaging (MAEMI) is trained on two non-contrast enhanced MRI sequences, aiming at lesion detection without the need for intravenous injection of contrast media and temporal image acquisition. During training, only non-cancerous images are presented to the model, with the purpose of localizing anomalous tumor regions during test time. We use a public dataset for model development. Performance of the architecture is evaluated in reference to subtraction images created from dynamic contrast enhanced (DCE)-MRI.},
	urldate = {2024-11-24},
	publisher = {arXiv},
	author = {Lang, Daniel M. and Schwartz, Eli and Bercea, Cosmin I. and Giryes, Raja and Schnabel, Julia A.},
	month = mar,
	year = {2023},
	note = {arXiv:2303.05861},
	keywords = {Electrical Engineering and Systems Science - Image and Video Processing},
}

@misc{zimmerer_unsupervised_2019,
	title = {Unsupervised {Anomaly} {Localization} using {Variational} {Auto}-{Encoders}},
	url = {http://arxiv.org/abs/1907.02796},
	doi = {10.48550/arXiv.1907.02796},
	abstract = {An assumption-free automatic check of medical images for potentially overseen anomalies would be a valuable assistance for a radiologist. Deep learning and especially Variational Auto-Encoders (VAEs) have shown great potential in the unsupervised learning of data distributions. In principle, this allows for such a check and even the localization of parts in the image that are most suspicious. Currently, however, the reconstruction-based localization by design requires adjusting the model architecture to the specific problem looked at during evaluation. This contradicts the principle of building assumption-free models. We propose complementing the localization part with a term derived from the Kullback-Leibler (KL)-divergence. For validation, we perform a series of experiments on FashionMNIST as well as on a medical task including {\textgreater}1000 healthy and {\textgreater}250 brain tumor patients. Results show that the proposed formalism outperforms the state of the art VAE-based localization of anomalies across many hyperparameter settings and also shows a competitive max performance.},
	urldate = {2024-11-24},
	publisher = {arXiv},
	author = {Zimmerer, David and Isensee, Fabian and Petersen, Jens and Kohl, Simon and Maier-Hein, Klaus},
	month = jul,
	year = {2019},
	note = {arXiv:1907.02796},
	keywords = {Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Statistics - Machine Learning},
}

@misc{ho_denoising_2020,
	title = {Denoising {Diffusion} {Probabilistic} {Models}},
	url = {http://arxiv.org/abs/2006.11239},
	doi = {10.48550/arXiv.2006.11239},
	abstract = {We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our implementation is available at https://github.com/hojonathanho/diffusion},
	urldate = {2024-11-24},
	publisher = {arXiv},
	author = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
	month = dec,
	year = {2020},
	note = {arXiv:2006.11239},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{noauthor_cancer_2015,
	title = {Cancer incidence statistics},
	howpublished= {\url{https://www.cancerresearchuk.org/health-professional/cancer-statistics/incidence}},
	abstract = {The latest cancer Incidence statistics for the UK for Health Professionals. See data for sex, age, trends over time and more.},
    author = {Cancer Research UK},
	language = {en},
	urldate = {2024-11-24},
	journal = {Cancer Research UK},
	month = may,
	year = {2015},
	note = {Accessed: 2024-11-24},
}

@misc{pinaya_unsupervised_2021,
	title = {Unsupervised {Brain} {Anomaly} {Detection} and {Segmentation} with {Transformers}},
	url = {http://arxiv.org/abs/2102.11650},
	doi = {10.48550/arXiv.2102.11650},
	abstract = {Pathological brain appearances may be so heterogeneous as to be intelligible only as anomalies, defined by their deviation from normality rather than any specific pathological characteristic. Amongst the hardest tasks in medical imaging, detecting such anomalies requires models of the normal brain that combine compactness with the expressivity of the complex, long-range interactions that characterise its structural organisation. These are requirements transformers have arguably greater potential to satisfy than other current candidate architectures, but their application has been inhibited by their demands on data and computational resource. Here we combine the latent representation of vector quantised variational autoencoders with an ensemble of autoregressive transformers to enable unsupervised anomaly detection and segmentation defined by deviation from healthy brain imaging data, achievable at low computational cost, within relative modest data regimes. We compare our method to current state-of-the-art approaches across a series of experiments involving synthetic and real pathological lesions. On real lesions, we train our models on 15,000 radiologically normal participants from UK Biobank, and evaluate performance on four different brain MR datasets with small vessel disease, demyelinating lesions, and tumours. We demonstrate superior anomaly detection performance both image-wise and pixel-wise, achievable without post-processing. These results draw attention to the potential of transformers in this most challenging of imaging tasks.},
	urldate = {2024-11-24},
	publisher = {arXiv},
	author = {Pinaya, Walter Hugo Lopez and Tudosiu, Petru-Daniel and Gray, Robert and Rees, Geraint and Nachev, Parashkev and Ourselin, Sebastien and others},
	month = feb,
	year = {2021},
	note = {arXiv:2102.11650},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Quantitative Methods},
}

@misc{liu_grounding_2024,
	title = {Grounding {DINO}: {Marrying} {DINO} with {Grounded} {Pre}-{Training} for {Open}-{Set} {Object} {Detection}},
	shorttitle = {Grounding {DINO}},
	url = {http://arxiv.org/abs/2303.05499},
	doi = {10.48550/arXiv.2303.05499},
	abstract = {In this paper, we present an open-set object detector, called Grounding DINO, by marrying Transformer-based detector DINO with grounded pre-training, which can detect arbitrary objects with human inputs such as category names or referring expressions. The key solution of open-set object detection is introducing language to a closed-set detector for open-set concept generalization. To effectively fuse language and vision modalities, we conceptually divide a closed-set detector into three phases and propose a tight fusion solution, which includes a feature enhancer, a language-guided query selection, and a cross-modality decoder for cross-modality fusion. While previous works mainly evaluate open-set object detection on novel categories, we propose to also perform evaluations on referring expression comprehension for objects specified with attributes. Grounding DINO performs remarkably well on all three settings, including benchmarks on COCO, LVIS, ODinW, and RefCOCO/+/g. Grounding DINO achieves a \$52.5\$ AP on the COCO detection zero-shot transfer benchmark, i.e., without any training data from COCO. It sets a new record on the ODinW zero-shot benchmark with a mean \$26.1\$ AP. Code will be available at {\textbackslash}url\{https://github.com/IDEA-Research/GroundingDINO\}.},
	urldate = {2024-11-24},
	publisher = {arXiv},
	author = {Liu, Shilong and Zeng, Zhaoyang and Ren, Tianhe and Li, Feng and Zhang, Hao and Yang, Jie and others},
	month = jul,
	year = {2024},
	note = {arXiv:2303.05499},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@article{litjens_survey_2017,
	title = {A survey on deep learning in medical image analysis},
	volume = {42},
	issn = {1361-8415},
	url = {https://www.sciencedirect.com/science/article/pii/S1361841517301135},
	doi = {10.1016/j.media.2017.07.005},
	abstract = {Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research.},
	urldate = {2024-11-20},
	journal = {Medical Image Analysis},
	author = {Litjens, Geert and Kooi, Thijs and Bejnordi, Babak Ehteshami and Setio, Arnaud Arindra Adiyoso and Ciompi, Francesco and Ghafoorian, Mohsen and others},
	month = dec,
	year = {2017},
	keywords = {Convolutional neural networks, Deep learning, Medical imaging, Survey},
	pages = {60--88},
}

@article{mcphail_stage_2015,
	title = {Stage at diagnosis and early mortality from cancer in {England}},
	volume = {112},
	copyright = {2015 The Author(s)},
	issn = {1532-1827},
	url = {https://www.nature.com/articles/bjc201549},
	doi = {10.1038/bjc.2015.49},
	abstract = {Stage at diagnosis is a key predictor of overall cancer outcome. For the first time, stage completeness is high enough for robust analysis for the whole of England.},
	language = {en},
	number = {1},
	urldate = {2024-11-20},
	journal = {British Journal of Cancer},
	author = {McPhail, S. and Johnson, S. and Greenberg, D. and Peake, M. and Rous, B.},
	month = mar,
	year = {2015},
	note = {Publisher: Nature Publishing Group},
	keywords = {Cancer epidemiology, Diagnosis},
	pages = {S108--S115},
}

@misc{noauthor_cancer_nodate,
	title = {Cancer survival by stage at diagnosis for {England} (experimental statistics) - {Office} for {National} {Statistics}},
	url = {https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/bulletins/cancersurvivalbystageatdiagnosisforenglandexperimentalstatistics/adultsdiagnosed20122013and2014andfollowedupto2015},
	urldate = {2024-11-20},
}

@misc{noauthor_cancer_nodate-1,
	title = {Cancer},
	howpublished = {\url{https://www.who.int/news-room/fact-sheets/detail/cancer}},
	abstract = {Cancer is a generic term for a large group of diseases that can affect any part of the body.},
    author = {World Health Organisation},
	language = {en},
    month = feb,
    year = {2022},
	urldate = {2024-11-20},
    note = {Accessed: 2024-11-20}
}

@article{maddams_projections_2012,
	title = {Projections of cancer prevalence in the {United} {Kingdom}, 2010–2040},
	volume = {107},
	issn = {0007-0920},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3461160/},
	doi = {10.1038/bjc.2012.366},
	abstract = {Background:
There are currently two million cancer survivors in the United Kingdom, and in recent years this number has grown by 3\% per annum. The aim of this paper is to provide long-term projections of cancer prevalence in the United Kingdom.

Methods:
National cancer registry data for England were used to estimate cancer prevalence in the United Kingdom in 2009. Using a model of prevalence as a function of incidence, survival and population demographics, projections were made to 2040. Different scenarios of future incidence and survival, and their effects on cancer prevalence, were also considered. Colorectal, lung, prostate, female breast and all cancers combined (excluding non-melanoma skin cancer) were analysed separately.

Results:
Assuming that existing trends in incidence and survival continue, the number of cancer survivors in the United Kingdom is projected to increase by approximately one million per decade from 2010 to 2040. Particularly large increases are anticipated in the oldest age groups, and in the number of long-term survivors. By 2040, almost a quarter of people aged at least 65 will be cancer survivors.

Conclusion:
Increasing cancer survival and the growing/ageing population of the United Kingdom mean that the population of survivors is likely to grow substantially in the coming decades, as are the related demands upon the health service. Plans must, therefore, be laid to ensure that the varied needs of cancer survivors can be met in the future.},
	number = {7},
	urldate = {2024-11-20},
	journal = {British Journal of Cancer},
	author = {Maddams, J and Utley, M and Møller, H},
	month = sep,
	year = {2012},
	pmid = {22892390},
	pmcid = {PMC3461160},
	pages = {1195--1202},
}

@article{robinson_radiologys_1997,
	title = {Radiology's {Achilles}' heel: error and variation in the interpretation of the {Röntgen} image},
	volume = {70},
	issn = {0007-1285},
	shorttitle = {Radiology's {Achilles}' heel},
	doi = {10.1259/bjr.70.839.9536897},
	abstract = {The performance of the human eye and brain has failed to keep pace with the enormous technical progress in the first full century of radiology. Errors and variations in interpretation now represent the weakest aspect of clinical imaging. Those interpretations which differ from the consensus view of a panel of "experts" may be regarded as errors; where experts fail to achieve consensus, differing reports are regarded as "observer variation". Errors arise from poor technique, failures of perception, lack of knowledge and misjudgments. Observer variation is substantial and should be taken into account when different diagnostic methods are compared; in many cases the difference between observers outweighs the difference between techniques. Strategies for reducing error include attention to viewing conditions, training of the observers, availability of previous films and relevant clinical data, dual or multiple reporting, standardization of terminology and report format, and assistance from computers. Digital acquisition and display will probably not affect observer variation but the performance of radiologists, as measured by receiver operating characteristic (ROC) analysis, may be improved by computer-directed search for specific image features. Other current developments show that where image features can be comprehensively described, computer analysis can replace the perception function of the observer, whilst the function of interpretation can in some cases be performed better by artificial neural networks. However, computer-assisted diagnosis is still in its infancy and complete replacement of the human observer is as yet a remote possibility.},
	language = {eng},
	number = {839},
	journal = {The British Journal of Radiology},
	author = {Robinson, P. J.},
	month = nov,
	year = {1997},
	pmid = {9536897},
	keywords = {Diagnostic Errors, Humans, Observer Variation, Radiographic Image Enhancement, Radiographic Image Interpretation, Computer-Assisted, Radiology},
	pages = {1085--1098},
}

@article{vaid_imaging_2011,
	title = {An imaging checklist for pre-{FESS} {CT}: framing a surgically relevant report},
	volume = {66},
	issn = {0009-9260},
	shorttitle = {An imaging checklist for pre-{FESS} {CT}},
	url = {https://www.sciencedirect.com/science/article/pii/S0009926011000031},
	doi = {10.1016/j.crad.2010.11.010},
	abstract = {The reference standard for preoperative imaging in functional endoscopic sinus surgery (FESS) is multiplanar high-resolution computed tomography (HRCT). Surgeons require a precise preoperative anatomical road map, and hence it is essential for radiologists to be familiar with the normal three-dimensional sinonasal anatomy and the normal variants encountered in this region. Sagittal imaging has recently emerged as an important tool to visualize additional details in this critical anatomical region. Radiologists also need to report these examinations with special focus on the surgeon's expectations. Constant communication between the radiologist and the surgeon helps to resolve specific issues and improve the overall quality of reports. This results in better preoperative patient counselling and in predicting postoperative improvement in clinical status. This review provides a basic structured format for reporting pre-FESS CT, which can be tailored to meet individual requirements. The CT reporting format follows the order in which the sinonasal structures are approached during surgery.},
	number = {5},
	urldate = {2024-11-19},
	journal = {Clinical Radiology},
	author = {Vaid, S. and Vaid, N. and Rawat, S. and Ahuja, A. T.},
	month = may,
	year = {2011},
	pages = {459--470},
}

@article{gawande_checklist_2011,
	title = {The {Checklist} {Manifesto}: {How} to {Get} {Things} {Right}},
	volume = {1},
	issn = {2155-8256},
	shorttitle = {The {Checklist} {Manifesto}},
	url = {https://www.journalofnursingregulation.com/article/S2155-8256(15)30310-0/fulltext},
	doi = {10.1016/S2155-8256(15)30310-0},
	language = {English},
	number = {4},
	urldate = {2024-11-19},
	journal = {Journal of Nursing Regulation},
	author = {Gawande, Atul},
	month = jan,
	year = {2011},
	note = {Publisher: Elsevier},
	pages = {64},
}

@article{berlin_accuracy_2007,
	title = {Accuracy of {Diagnostic} {Procedures}: {Has} {It} {Improved} {Over} the {Past} {Five} {Decades}?},
	volume = {188},
	issn = {0361-803X},
	shorttitle = {Accuracy of {Diagnostic} {Procedures}},
	url = {https://www.ajronline.org/doi/full/10.2214/AJR.06.1270},
	doi = {10.2214/AJR.06.1270},
	number = {5},
	urldate = {2024-11-19},
	journal = {American Journal of Roentgenology},
	author = {Berlin, Leonard},
	month = may,
	year = {2007},
	note = {Publisher: American Roentgen Ray Society},
	pages = {1173--1178},
}

@article{kim_fool_2014,
	title = {Fool {Me} {Twice}: {Delayed} {Diagnoses} in {Radiology} {With} {Emphasis} on {Perpetuated} {Errors}},
	volume = {202},
	issn = {0361-803X},
	shorttitle = {Fool {Me} {Twice}},
	url = {https://www.ajronline.org/doi/10.2214/AJR.13.11493},
	doi = {10.2214/AJR.13.11493},
	abstract = {OBJECTIVE. We hypothesized that delayed diagnoses in radiology are not recognized on subsequent radiologic examinations because of multiple types of errors.
MATERIALS AND METHODS. Six hundred fifty-six radiologic examinations with delayed diagnoses were collected from July 1, 2002, to January 31, 2010. Each case was reviewed by two radiologists together, and the diagnostic errors were classified according to our modified scheme with consensus between the radiologists.
RESULTS. There were a total of 1269 errors. The range of days elapsed from the initial error in interpretation to the correct diagnosis was 0–4611 days, with an average of 251 days. The percentage for each type of error was 0.9\% (n = 11) for type 1, 9\% (n = 110) for type 2, 3\% (n = 39) for type 3, 42\% (n = 535) for type 4, approximately 0\% (n = 1) for type 5, 2\% (n = 29) for type 6, 5\% (n = 59) for type 7, 2\% (n = 20) for type 8, 7\% (n = 92) for type 9, 22\% (n = 288) for type 10, 0.5\% (n = 6) for type 11, and 6\% (n = 79) for type 12. The correct diagnoses were not recognized on subsequent radiologic examinations in 196 of 656 cases (30\%).
CONCLUSION. Delayed diagnoses were not recognized on subsequent radiologic examinations in about one third of the cases. The most common types of error were underreading, satisfaction of search, faulty reasoning, and location of the finding.},
	number = {3},
	urldate = {2024-11-19},
	journal = {American Journal of Roentgenology},
	author = {Kim, Young W. and Mansfield, Liem T.},
	month = mar,
	year = {2014},
	note = {Publisher: American Roentgen Ray Society},
	pages = {465--470},
}

@misc{grauw_uls23_2024,
	title = {The {ULS23} {Challenge}: a {Baseline} {Model} and {Benchmark} {Dataset} for {3D} {Universal} {Lesion} {Segmentation} in {Computed} {Tomography}},
	shorttitle = {The {ULS23} {Challenge}},
	url = {http://arxiv.org/abs/2406.05231},
	doi = {10.48550/arXiv.2406.05231},
	abstract = {Size measurements of tumor manifestations on follow-up CT examinations are crucial for evaluating treatment outcomes in cancer patients. Efficient lesion segmentation can speed up these radiological workflows. While numerous benchmarks and challenges address lesion segmentation in specific organs like the liver, kidneys, and lungs, the larger variety of lesion types encountered in clinical practice demands a more universal approach. To address this gap, we introduced the ULS23 benchmark for 3D universal lesion segmentation in chest-abdomen-pelvis CT examinations. The ULS23 training dataset contains 38,693 lesions across this region, including challenging pancreatic, colon and bone lesions. For evaluation purposes, we curated a dataset comprising 775 lesions from 284 patients. Each of these lesions was identified as a target lesion in a clinical context, ensuring diversity and clinical relevance within this dataset. The ULS23 benchmark is publicly accessible via uls23.grand-challenge.org, enabling researchers worldwide to assess the performance of their segmentation methods. Furthermore, we have developed and publicly released our baseline semi-supervised 3D lesion segmentation model. This model achieved an average Dice coefficient of 0.703 \${\textbackslash}pm\$ 0.240 on the challenge test set. We invite ongoing submissions to advance the development of future ULS models.},
	urldate = {2024-11-19},
	publisher = {arXiv},
	author = {Grauw, M. J. J. de and Scholten, E. Th and Smit, E. J. and Rutten, M. J. C. M. and Prokop, M. and Ginneken, B. van and others},
	month = jun,
	year = {2024},
	note = {arXiv:2406.05231},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing},
}

@misc{schluter_natural_2022,
	title = {Natural {Synthetic} {Anomalies} for {Self}-{Supervised} {Anomaly} {Detection} and {Localization}},
	url = {http://arxiv.org/abs/2109.15222},
	abstract = {We introduce a simple and intuitive self-supervision task, Natural Synthetic Anomalies (NSA), for training an end-to-end model for anomaly detection and localization using only normal training data. NSA integrates Poisson image editing to seamlessly blend scaled patches of various sizes from separate images. This creates a wide range of synthetic anomalies which are more similar to natural sub-image irregularities than previous data-augmentation strategies for self-supervised anomaly detection. We evaluate the proposed method using natural and medical images. Our experiments with the MVTec AD dataset show that a model trained to localize NSA anomalies generalizes well to detecting real-world a priori unknown types of manufacturing defects. Our method achieves an overall detection AUROC of 97.2 outperforming all previous methods that learn without the use of additional datasets. Code available at https://github.com/hmsch/natural-synthetic-anomalies.},
	urldate = {2024-11-19},
	publisher = {arXiv},
	author = {Schlüter, Hannah M. and Tan, Jeremy and Hou, Benjamin and Kainz, Bernhard},
	month = jul,
	year = {2022},
	note = {arXiv:2109.15222},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@misc{bercea_diffusion_2024,
	title = {Diffusion {Models} with {Implicit} {Guidance} for {Medical} {Anomaly} {Detection}},
	url = {http://arxiv.org/abs/2403.08464},
	abstract = {Diffusion models have advanced unsupervised anomaly detection by improving the transformation of pathological images into pseudo-healthy equivalents. Nonetheless, standard approaches may compromise critical information during pathology removal, leading to restorations that do not align with unaffected regions in the original scans. Such discrepancies can inadvertently increase false positive rates and reduce specificity, complicating radiological evaluations. This paper introduces Temporal Harmonization for Optimal Restoration (THOR), which refines the de-noising process by integrating implicit guidance through temporal anomaly maps. THOR aims to preserve the integrity of healthy tissue in areas unaffected by pathology. Comparative evaluations show that THOR surpasses existing diffusion-based methods in detecting and segmenting anomalies in brain MRIs and wrist X-rays. Code: https://github.com/ci-ber/THOR\_DDPM.},
	urldate = {2024-11-19},
	publisher = {arXiv},
	author = {Bercea, Cosmin I. and Wiestler, Benedikt and Rueckert, Daniel and Schnabel, Julia A.},
	month = mar,
	year = {2024},
	note = {arXiv:2403.08464},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing},
}

@misc{wolleb_diffusion_2022,
	title = {Diffusion {Models} for {Medical} {Anomaly} {Detection}},
	url = {http://arxiv.org/abs/2203.04306},
	abstract = {In medical applications, weakly supervised anomaly detection methods are of great interest, as only image-level annotations are required for training. Current anomaly detection methods mainly rely on generative adversarial networks or autoencoder models. Those models are often complicated to train or have difficulties to preserve fine details in the image. We present a novel weakly supervised anomaly detection method based on denoising diffusion implicit models. We combine the deterministic iterative noising and denoising scheme with classifier guidance for image-to-image translation between diseased and healthy subjects. Our method generates very detailed anomaly maps without the need for a complex training procedure. We evaluate our method on the BRATS2020 dataset for brain tumor detection and the CheXpert dataset for detecting pleural effusions.},
	urldate = {2024-11-19},
	publisher = {arXiv},
	author = {Wolleb, Julia and Bieder, Florentin and Sandkühler, Robin and Cattin, Philippe C.},
	month = oct,
	year = {2022},
	note = {arXiv:2203.04306},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing},
}

@misc{bercea_towards_2024,
	title = {Towards {Universal} {Unsupervised} {Anomaly} {Detection} in {Medical} {Imaging}},
	url = {http://arxiv.org/abs/2401.10637},
	abstract = {The increasing complexity of medical imaging data underscores the need for advanced anomaly detection methods to automatically identify diverse pathologies. Current methods face challenges in capturing the broad spectrum of anomalies, often limiting their use to specific lesion types in brain scans. To address this challenge, we introduce a novel unsupervised approach, termed {\textbackslash}textit\{Reversed Auto-Encoders (RA)\}, designed to create realistic pseudo-healthy reconstructions that enable the detection of a wider range of pathologies. We evaluate the proposed method across various imaging modalities, including magnetic resonance imaging (MRI) of the brain, pediatric wrist X-ray, and chest X-ray, and demonstrate superior performance in detecting anomalies compared to existing state-of-the-art methods. Our unsupervised anomaly detection approach may enhance diagnostic accuracy in medical imaging by identifying a broader range of unknown pathologies. Our code is publicly available at: {\textbackslash}url\{https://github.com/ci-ber/RA\}.},
	urldate = {2024-11-19},
	publisher = {arXiv},
	author = {Bercea, Cosmin I. and Wiestler, Benedikt and Rueckert, Daniel and Schnabel, Julia A.},
	month = jan,
	year = {2024},
	note = {arXiv:2401.10637},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing},
}

@incollection{crimi_deepmedic_2016,
	address = {Cham},
	title = {{DeepMedic} for {Brain} {Tumor} {Segmentation}},
	volume = {10154},
	isbn = {978-3-319-55523-2 978-3-319-55524-9},
	url = {http://link.springer.com/10.1007/978-3-319-55524-9_14},
	abstract = {Accurate automatic algorithms for the segmentation of brain tumours have the potential of improving disease diagnosis, treatment planning, as well as enabling large-scale studies of the pathology. In this work we employ DeepMedic [1], a 3D CNN architecture previously presented for lesion segmentation, which we further improve by adding residual connections. We also present a series of experiments on the BRATS 2015 training database for evaluating the robustness of the network when less training data are available or less ﬁlters are used, aiming to shed some light on requirements for employing such a system. Our method was further benchmarked on the BRATS 2016 Challenge, where it achieved very good performance despite the simplicity of the pipeline.},
	language = {en},
	urldate = {2024-11-19},
	booktitle = {Brainlesion: {Glioma}, {Multiple} {Sclerosis}, {Stroke} and {Traumatic} {Brain} {Injuries}},
	publisher = {Springer International Publishing},
	author = {Kamnitsas, Konstantinos and Ferrante, Enzo and Parisot, Sarah and Ledig, Christian and Nori, Aditya V. and Criminisi, Antonio and others},
	editor = {Crimi, Alessandro and Menze, Bjoern and Maier, Oskar and Reyes, Mauricio and Winzeck, Stefan and Handels, Heinz},
	year = {2016},
	doi = {10.1007/978-3-319-55524-9_14},
	note = {Series Title: Lecture Notes in Computer Science},
	pages = {138--149},
}

@article{antun_instabilities_2020,
	title = {On instabilities of deep learning in image reconstruction and the potential costs of {AI}},
	volume = {117},
	url = {https://www.pnas.org/doi/10.1073/pnas.1907377117},
	doi = {10.1073/pnas.1907377117},
	abstract = {Deep learning, due to its unprecedented success in tasks such as image classification, has emerged as a new tool in image reconstruction with potential to change the field. In this paper, we demonstrate a crucial phenomenon: Deep learning typically yields unstable methods for image reconstruction. The instabilities usually occur in several forms: 1) Certain tiny, almost undetectable perturbations, both in the image and sampling domain, may result in severe artefacts in the reconstruction; 2) a small structural change, for example, a tumor, may not be captured in the reconstructed image; and 3) (a counterintuitive type of instability) more samples may yield poorer performance. Our stability test with algorithms and easy-to-use software detects the instability phenomena. The test is aimed at researchers, to test their networks for instabilities, and for government agencies, such as the Food and Drug Administration (FDA), to secure safe use of deep learning methods.},
	number = {48},
	urldate = {2024-11-19},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Antun, Vegard and Renna, Francesco and Poon, Clarice and Adcock, Ben and Hansen, Anders C.},
	month = dec,
	year = {2020},
	note = {Publisher: Proceedings of the National Academy of Sciences},
	pages = {30088--30095},
}

@misc{nhs_scans_statistics,
	title = {Diagnostic Imaging Dataset Statistical Release},
	howpublished= {\url{https://www.england.nhs.uk/statistics/wp-content/uploads/sites/2/2023/05/Statistical-Release-18th-May-2023-PDF-471KB-1.pdf}},
	abstract = {The latest cancer Incidence statistics for the UK for Health Professionals. See data for sex, age, trends over time and more.},
    author = {NHS England},
	language = {en},
	urldate = {2025-05-19},
	journal = {Cancer Research UK},
	month = may,
	year = {2023},
	note = {Accessed: 2025-05-19},
}

@article{khader_importance_2022,
	title = {Importance of tumor subtypes in cancer imaging},
	volume = {9},
	issn = {2352-0477},
	url = {https://www.ejropen.com/article/S2352-0477%2822%2900040-5/fulltext?utm_source=chatgpt.com},
	doi = {10.1016/j.ejro.2022.100433},
	language = {English},
	urldate = {2025-05-19},
	journal = {European Journal of Radiology Open},
	author = {Khader, Ali and Braschi-Amirfarzan, Marta and McIntosh, Lacey J. and Gosangi, Babina and Wortman, Jeremy R. and Wald, Christoph and others},
	month = jan,
	year = {2022},
	pmid = {35265736},
	note = {Publisher: Elsevier},
	keywords = {Artificial intelligence, Cancer, Precision medicine, Radiomics, Tumor subtype},
}

@article{wu_identifying_2017,
	title = {Identifying relations between imaging phenotypes and molecular subtypes of breast cancer: {Model} discovery and external validation},
	volume = {46},
	copyright = {© 2017 International Society for Magnetic Resonance in Medicine},
	issn = {1522-2586},
	shorttitle = {Identifying relations between imaging phenotypes and molecular subtypes of breast cancer},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jmri.25661},
	doi = {10.1002/jmri.25661},
	abstract = {Purpose To determine whether dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) characteristics of the breast tumor and background parenchyma can distinguish molecular subtypes (ie, luminal A/B or basal) of breast cancer. Materials and Methods In all, 84 patients from one institution and 126 patients from The Cancer Genome Atlas (TCGA) were used for discovery and external validation, respectively. Thirty-five quantitative image features were extracted from DCE-MRI (1.5 or 3T) including morphology, texture, and volumetric features, which capture both tumor and background parenchymal enhancement (BPE) characteristics. Multiple testing was corrected using the Benjamini–Hochberg method to control the false-discovery rate (FDR). Sparse logistic regression models were built using the discovery cohort to distinguish each of the three studied molecular subtypes versus the rest, and the models were evaluated in the validation cohort. Results On univariate analysis in discovery and validation cohorts, two features characterizing tumor and two characterizing BPE were statistically significant in separating luminal A versus nonluminal A cancers; two features characterizing tumor were statistically significant for separating luminal B; one feature characterizing tumor and one characterizing BPE reached statistical significance for distinguishing basal (Wilcoxon P {\textless} 0.05, FDR {\textless} 0.25). In discovery and validation cohorts, multivariate logistic regression models achieved an area under the receiver operator characteristic curve (AUC) of 0.71 and 0.73 for luminal A cancer, 0.67 and 0.69 for luminal B cancer, and 0.66 and 0.79 for basal cancer, respectively. Conclusion DCE-MRI characteristics of breast cancer and BPE may potentially be used to distinguish among molecular subtypes of breast cancer. Level of Evidence: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1017–1027.},
	language = {en},
	number = {4},
	urldate = {2025-05-19},
	journal = {Journal of Magnetic Resonance Imaging},
	author = {Wu, Jia and Sun, Xiaoli and Wang, Jeff and Cui, Yi and Kato, Fumi and Shirato, Hiroki and others},
	year = {2017},
	note = {},
	keywords = {breast cancer, classification, dynamic contrast enhanced MRI, imaging genomics, molecular subtype},
	pages = {1017--1027},
}

@inproceedings{
bercea2023generalizing,
title={Generalizing Unsupervised Anomaly Detection: Towards Unbiased Pathology Screening},
author = {Cosmin I. Bercea and Benedikt Wiestler and Daniel Rueckert and Julia A Schnabel},
booktitle={Medical Imaging with Deep Learning},
year={2023},
url={https://openreview.net/forum?id=8ojx-Ld3yjR}
}

@misc{huang_adapting_2024,
	title = {Adapting {Visual}-{Language} {Models} for {Generalizable} {Anomaly} {Detection} in {Medical} {Images}},
	url = {http://arxiv.org/abs/2403.12570},
	doi = {10.48550/arXiv.2403.12570},
	abstract = {Recent advancements in large-scale visual-language pre-trained models have led to significant progress in zero-/few-shot anomaly detection within natural image domains. However, the substantial domain divergence between natural and medical images limits the effectiveness of these methodologies in medical anomaly detection. This paper introduces a novel lightweight multi-level adaptation and comparison framework to repurpose the CLIP model for medical anomaly detection. Our approach integrates multiple residual adapters into the pre-trained visual encoder, enabling a stepwise enhancement of visual features across different levels. This multi-level adaptation is guided by multi-level, pixel-wise visual-language feature alignment loss functions, which recalibrate the model's focus from object semantics in natural imagery to anomaly identification in medical images. The adapted features exhibit improved generalization across various medical data types, even in zero-shot scenarios where the model encounters unseen medical modalities and anatomical regions during training. Our experiments on medical anomaly detection benchmarks demonstrate that our method significantly surpasses current state-of-the-art models, with an average AUC improvement of 6.24\% and 7.33\% for anomaly classification, 2.03\% and 2.37\% for anomaly segmentation, under the zero-shot and few-shot settings, respectively. Source code is available at: https://github.com/MediaBrain-SJTU/MVFA-AD},
	urldate = {2025-05-19},
	publisher = {arXiv},
	author = {Huang, Chaoqin and Jiang, Aofan and Feng, Jinghao and Zhang, Ya and Wang, Xinchao and Wang, Yanfeng},
	month = mar,
	year = {2024},
	note = {arXiv:2403.12570 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@article{sobek_medyolo_2024,
	title = {{MedYOLO}: {A} {Medical} {Image} {Object} {Detection} {Framework}},
	volume = {37},
	issn = {2948-2933},
	shorttitle = {{MedYOLO}},
	url = {http://arxiv.org/abs/2312.07729},
	doi = {10.1007/s10278-024-01138-2},
	abstract = {Artificial intelligence-enhanced identification of organs, lesions, and other structures in medical imaging is typically done using convolutional neural networks (CNNs) designed to make voxel-accurate segmentations of the region of interest. However, the labels required to train these CNNs are time-consuming to generate and require attention from subject matter experts to ensure quality. For tasks where voxel-level precision is not required, object detection models offer a viable alternative that can reduce annotation effort. Despite this potential application, there are few options for general purpose object detection frameworks available for 3-D medical imaging. We report on MedYOLO, a 3-D object detection framework using the one-shot detection method of the YOLO family of models and designed for use with medical imaging. We tested this model on four different datasets: BRaTS, LIDC, an abdominal organ Computed Tomography (CT) dataset, and an ECG-gated heart CT dataset. We found our models achieve high performance on commonly present medium and large-sized structures such as the heart, liver, and pancreas even without hyperparameter tuning. However, the models struggle with very small or rarely present structures.},
	number = {6},
	urldate = {2025-05-19},
	journal = {Journal of Imaging Informatics in Medicine},
	author = {Sobek, Joseph and Inojosa, Jose R. Medina and Inojosa, Betsy J. Medina and Rassoulinejad-Mousavi, S. M. and Conte, Gian Marco and Lopez-Jimenez, Francisco and others},
	month = jun,
	year = {2024},
	note = {arXiv:2312.07729 [eess]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing},
	pages = {3208--3216},
}

@misc{radford_learning_2021,
	title = {Learning {Transferable} {Visual} {Models} {From} {Natural} {Language} {Supervision}},
	url = {http://arxiv.org/abs/2103.00020},
	doi = {10.48550/arXiv.2103.00020},
	abstract = {State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP.},
	urldate = {2025-05-19},
	publisher = {arXiv},
	author = {Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and others},
	month = feb,
	year = {2021},
	note = {arXiv:2103.00020 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@software{yolo11_ultralytics,
  author = {Glenn Jocher and Jing Qiu},
  title = {Ultralytics YOLO11},
  version = {11.0.0},
  year = {2024},
  url = {https://github.com/ultralytics/ultralytics},
  orcid = {0000-0001-5950-6979, 0000-0002-7603-6750, 0000-0003-3783-7069},
  license = {AGPL-3.0}
}


@misc{khanam_yolov11_2024,
	title = {{YOLOv11}: {An} {Overview} of the {Key} {Architectural} {Enhancements}},
	shorttitle = {{YOLOv11}},
	url = {http://arxiv.org/abs/2410.17725},
	doi = {10.48550/arXiv.2410.17725},
	abstract = {This study presents an architectural analysis of YOLOv11, the latest iteration in the YOLO (You Only Look Once) series of object detection models. We examine the models architectural innovations, including the introduction of the C3k2 (Cross Stage Partial with kernel size 2) block, SPPF (Spatial Pyramid Pooling - Fast), and C2PSA (Convolutional block with Parallel Spatial Attention) components, which contribute in improving the models performance in several ways such as enhanced feature extraction. The paper explores YOLOv11's expanded capabilities across various computer vision tasks, including object detection, instance segmentation, pose estimation, and oriented object detection (OBB). We review the model's performance improvements in terms of mean Average Precision (mAP) and computational efficiency compared to its predecessors, with a focus on the trade-off between parameter count and accuracy. Additionally, the study discusses YOLOv11's versatility across different model sizes, from nano to extra-large, catering to diverse application needs from edge devices to high-performance computing environments. Our research provides insights into YOLOv11's position within the broader landscape of object detection and its potential impact on real-time computer vision applications.},
	urldate = {2025-05-23},
	publisher = {arXiv},
	author = {Khanam, Rahima and Hussain, Muhammad},
	month = oct,
	year = {2024},
	note = {arXiv:2410.17725 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@misc{rao_yolov11_2024,
	title = {{YOLOv11} {Explained}: {Next}-{Level} {Object} {Detection} with {Enhanced} {Speed} and {Accuracy}},
    howpublished = {\url{https://medium.com/@nikhil-rao-20/yolov11-explained-next-level-object-detection-with-enhanced-speed-and-accuracy-2dbe2d376f71}},
	shorttitle = {{YOLOv11} {Explained}},
	abstract = {A brief article all about the recently released YOLOv11 from its architecture to its performance. This is all you want to know about.},
	language = {en},
	urldate = {2025-05-23},
	journal = {Medium},
	author = {Rao, S. Nikhileswara},
	month = oct,
	year = {2024},
    note = {Accessed: 2025-05-23}
}

@misc{team_gemini_2025,
	title = {Gemini: {A} {Family} of {Highly} {Capable} {Multimodal} {Models}},
	shorttitle = {Gemini},
	url = {http://arxiv.org/abs/2312.11805},
	doi = {10.48550/arXiv.2312.11805},
	abstract = {This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.},
	urldate = {2025-05-25},
	publisher = {arXiv},
	author = {Team, Gemini and Anil, Rohan and Borgeaud, Sebastian and Alayrac, Jean-Baptiste and Yu, Jiahui and Soricut, Radu and others},
	month = may,
	year = {2025},
	note = {arXiv:2312.11805 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition},
}


@misc{Open_Grounding_Dino,
  author = {Zuwei Long, Wei Li},
  title = {Open Grounding Dino:The third party implementation of the paper Grounding DINO},
  howpublished = {\url{https://github.com/longzw1997/Open-GroundingDino}},
  year = {2023}
}


@article{seibert_x-ray_2005,
	title = {X-{Ray} {Imaging} {Physics} for {Nuclear} {Medicine} {Technologists}. {Part} 2: {X}-{Ray} {Interactions} and {Image} {Formation}},
	volume = {33},
	copyright = {© 2005},
	issn = {0091-4916, 1535-5675},
	shorttitle = {X-{Ray} {Imaging} {Physics} for {Nuclear} {Medicine} {Technologists}. {Part} 2},
	url = {https://tech.snmjournals.org/content/33/1/3},
	abstract = {The purpose is to review in a 4-part series: (i) the basic principles of x-ray production, (ii) x-ray interactions and data capture/conversion, (iii) acquisition/creation of the CT image, and (iv) operational details of a modern multislice CT scanner integrated with a PET scanner. In part 1, the production and characteristics of x-rays were reviewed. In this article, the principles of x-ray interactions and image formation are discussed, in preparation for a general review of CT (part 3) and a more detailed investigation of PET/CT scanners in part 4.},
	language = {en},
	number = {1},
	urldate = {2025-05-26},
	journal = {Journal of Nuclear Medicine Technology},
	author = {Seibert, J. Anthony and Boone, John M.},
	month = mar,
	year = {2005},
	pmid = {15731015},
	note = {Publisher: Society of Nuclear Medicine
Section: CONTINUING EDUCATION},
	keywords = {CT, attenuation, digital x-ray detectors, screen-film x-ray detectors, x-ray detection, x-ray image formation, x-ray interactions, γ-ray interactions},
	pages = {3--18},
}


@incollection{bibb_2_2015,
	address = {Oxford},
	edition = {Second Edition},
	title = {2 - {Medical} imaging},
	isbn = {978-1-78242-300-3},
	url = {https://www.sciencedirect.com/science/article/pii/B9781782423003000020},
	abstract = {This chapter introduces the acquisition of medical data for use in medical modelling, usually referred to as medical imaging. Three-dimensional forms of medical imaging are described, including computed tomography (CT), cone beam computed tomography (CBCT), magnetic resonance imaging (MRI) and optical light/laser based surface scanners. The characteristics, advantages and disadvantages are given for each modality. CT is generally good for imaging bones, whilst MRI is better at imaging internal organs. CBCT is useful for dental and facial imaging and has a lower X-ray dosage compared to conventional CT. Issues relating to obtaining good quality image data are discussed including ensuring good anatomical coverage, field of view, resolution and reducing errors due to movement, artefacts and noise. The various settings and parameters possible are introduced. The chapter also describes the format of the resulting image data including digital imaging and communications in medicine medical images and point cloud data. The use of the data is then described in Chapter 3.},
	booktitle = {Medical {Modelling} ({Second} {Edition})},
	publisher = {Woodhead Publishing},
	author = {Bibb, Richard and Eggbeer, Dominic and Paterson, Abby},
	editor = {Bibb, Richard and Eggbeer, Dominic and Paterson, Abby},
	year = {2015},
	doi = {https://doi.org/10.1016/B978-1-78242-300-3.00002-0},
	keywords = {Artefacts, CBCT, CT, DICOM, Laser scanning, MRI, Medical imaging, Noise, Point cloud data},
	pages = {7--34},
}


@article{burgess_rose_1999,
	title = {The {Rose} model, revisited},
	volume = {16},
	copyright = {© 1999 Optical Society of America},
	issn = {1520-8532},
	url = {https://opg.optica.org/josaa/abstract.cfm?uri=josaa-16-3-633},
	doi = {10.1364/JOSAA.16.000633},
	abstract = {In 1946 and 1948, three very important papers by Albert Rose [J. Soc. Motion Pict. Eng.47, 273 (1946); J. Opt. Soc. Am.38, 196 (1948); MartonL., ed. (Academic, New York, 1948)] were published on the role that photon fluctuations have in setting fundamental performance limits for both human vision and electronic imaging systems. The papers were important because Rose demonstrated that the performance of imaging devices can be evaluated with an absolute scale (quantum efficiency). The analysis of human visual signal detection used in these papers (developed before the formal theory of signal detectability) was based on an approach that has come to be known as the Rose model. In spite of its simplicity, the Rose model is a very good approximation of a Bayesian ideal observer for the carefully and narrowly defined conditions that Rose considered. This simple model can be used effectively for back-of-the-envelope calculations, but it needs to be used with care because of its limited range of validity. One important conclusion arising from Rose’s investigations is that pixel signal-to-noise ratio is not a good figure of merit for imaging systems or components, even though it is still occasionally used as such by some researchers. In the present study, (1) aspects of signal detection theory are presented, (2) Rose’s model is described and discussed, (3) pixel signal-to-noise ratio is discussed, and (4) progress on modeling human noise-limited performance is summarized. This study is intended to be a tutorial with presentation of the main ideas and provision of references to the (dispersed) technical literature.},
	language = {EN},
	number = {3},
	urldate = {2025-05-29},
	journal = {JOSA A},
	author = {Burgess, Arthur E.},
	month = mar,
	year = {1999},
	note = {Publisher: Optica Publishing Group},
	keywords = {Biomedical imaging, Fourier transforms, Imaging systems, Imaging techniques, Modulation transfer function, Pattern recognition},
	pages = {633--646},
}

@article{zhao_biomedparse_2025,
	title = {{BiomedParse}: a biomedical foundation model for image parsing of everything everywhere all at once},
	volume = {22},
	issn = {1548-7091, 1548-7105},
	shorttitle = {{BiomedParse}},
	url = {http://arxiv.org/abs/2405.12971},
	doi = {10.1038/s41592-024-02499-w},
	abstract = {Biomedical image analysis is fundamental for biomedical discovery in cell biology, pathology, radiology, and many other biomedical domains. Holistic image analysis comprises interdependent subtasks such as segmentation, detection, and recognition of relevant objects. Here, we propose BiomedParse, a biomedical foundation model for imaging parsing that can jointly conduct segmentation, detection, and recognition for 82 object types across 9 imaging modalities. Through joint learning, we can improve accuracy for individual tasks and enable novel applications such as segmenting all relevant objects in an image through a text prompt, rather than requiring users to laboriously specify the bounding box for each object. We leveraged readily available natural-language labels or descriptions accompanying those datasets and use GPT-4 to harmonize the noisy, unstructured text information with established biomedical object ontologies. We created a large dataset comprising over six million triples of image, segmentation mask, and textual description. On image segmentation, we showed that BiomedParse is broadly applicable, outperforming state-of-the-art methods on 102,855 test image-mask-label triples across 9 imaging modalities (everything). On object detection, which aims to locate a specific object of interest, BiomedParse again attained state-of-the-art performance, especially on objects with irregular shapes (everywhere). On object recognition, which aims to identify all objects in a given image along with their semantic types, we showed that BiomedParse can simultaneously segment and label all biomedical objects in an image (all at once). In summary, BiomedParse is an all-in-one tool for biomedical image analysis by jointly solving segmentation, detection, and recognition for all major biomedical image modalities, paving the path for efficient and accurate image-based biomedical discovery.},
	number = {1},
	urldate = {2025-06-11},
	journal = {Nature Methods},
	author = {Zhao, Theodore and Gu, Yu and Yang, Jianwei and Usuyama, Naoto and Lee, Ho Hin and Naumann, Tristan and others},
	month = jan,
	year = {2025},
	note = {arXiv:2405.12971 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	pages = {166--176},
}

@misc{wolf_huggingfaces_2020,
	title = {{HuggingFace}'s {Transformers}: {State}-of-the-art {Natural} {Language} {Processing}},
	shorttitle = {{HuggingFace}'s {Transformers}},
	url = {http://arxiv.org/abs/1910.03771},
	doi = {10.48550/arXiv.1910.03771},
	abstract = {Recent progress in natural language processing has been driven by advances in both model architecture and model pretraining. Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to effectively utilize this capacity for a wide variety of tasks. {\textbackslash}textit\{Transformers\} is an open-source library with the goal of opening up these advances to the wider machine learning community. The library consists of carefully engineered state-of-the art Transformer architectures under a unified API. Backing this library is a curated collection of pretrained models made by and available for the community. {\textbackslash}textit\{Transformers\} is designed to be extensible by researchers, simple for practitioners, and fast and robust in industrial deployments. The library is available at {\textbackslash}url\{https://github.com/huggingface/transformers\}.},
	urldate = {2025-06-21},
	publisher = {arXiv},
	author = {Wolf, Thomas and Debut, Lysandre and Sanh, Victor and Chaumond, Julien and Delangue, Clement and Moi, Anthony and others},
	month = jul,
	year = {2020},
	note = {arXiv:1910.03771 [cs]},
	keywords = {Computer Science - Computation and Language},
}

@misc{devlin_bert_2019,
	title = {{BERT}: {Pre}-training of {Deep} {Bidirectional} {Transformers} for {Language} {Understanding}},
	shorttitle = {{BERT}},
	url = {http://arxiv.org/abs/1810.04805},
	doi = {10.48550/arXiv.1810.04805},
	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).},
	urldate = {2025-06-21},
	publisher = {arXiv},
	author = {Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
	month = may,
	year = {2019},
	note = {arXiv:1810.04805 [cs]},
	keywords = {Computer Science - Computation and Language},
}
@article{luo2024biomedgpt,
  title={Biomedgpt: An open multimodal large language model for biomedicine},
  author={Luo, Yizhen and Zhang, Jiahuan and Fan, Siqi and Yang, Kai and Hong, Massimo and Wu, Yushuai and Qiao, Mu and Nie, Zaiqing},
  journal={IEEE Journal of Biomedical and Health Informatics},
  year={2024},
  publisher={IEEE}
}
@article{achiam2023gpt,
  title={Gpt-4 technical report},
  author={Achiam, Josh and Adler, Steven and Agarwal, Sandhini and Ahmad, Lama and Akkaya, Ilge and Aleman, Florencia Leoni and Almeida, Diogo and Altenschmidt, Janko and Altman, Sam and Anadkat, Shyamal and others},
  journal={arXiv preprint arXiv:2303.08774},
  year={2023}
}
@article{liu2025segment,
  title={Segment Any Tissue: One-shot reference guided training-free automatic point prompting for medical image segmentation},
  author={Liu, Xueyu and Shi, Guangze and Wang, Rui and Lai, Yexin and Zhang, Jianan and Han, Weixia and Lei, Min and Li, Ming and Zhou, Xiaoshuang and Wu, Yongfei and others},
  journal={Medical Image Analysis},
  volume={102},
  pages={103550},
  year={2025},
  publisher={Elsevier}
}
@inproceedings{aleem2024test,
  title={Test-time adaptation with salip: A cascade of sam and clip for zero-shot medical image segmentation},
  author={Aleem, Sidra and Wang, Fangyijie and Maniparambil, Mayug and Arazo, Eric and Dietlmeier, Julia and Curran, Kathleen and Connor, Noel EO' and Little, Suzanne},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={5184--5193},
  year={2024}
}