@book{Lamport:Book:1989,
 author = {Lamport, Leslie},
 title = {Latex: A Document Preparation System},
 year = {1986},
 isbn = {0-201-15790-X},
 publisher = {Addison-Wesley Longman Publishing Co., Inc.},
 address = {Boston, MA, USA},
}

@misc{Hinton:arXiv:2015:Distilling,
 title={Distilling the knowledge in a neural network},
 author={Hinton, Geoffrey and Vinyals, Oriol and Dean, Jeff},
 OPTeprinttype={arXiv},
 OPTeprint={1503.02531},
 howpublished={Eprint \href{http://arxiv.org/abs/1503.02531}{arXiv:1503.02531}},
 OPTurl={http://arxiv.org/abs/1503.02531},
 year=2015
}



@String(PAMI = {IEEE Trans. Pattern Anal. Mach. Intell.})
@String(IJCV = {Int. J. Comput. Vis.})
@String(CVPR= {IEEE Conf. Comput. Vis. Pattern Recog.})
@String(ICCV= {Int. Conf. Comput. Vis.})
@String(ECCV= {Eur. Conf. Comput. Vis.})
@String(NIPS= {Adv. Neural Inform. Process. Syst.})
@String(ICPR = {Int. Conf. Pattern Recog.})
@String(BMVC= {Brit. Mach. Vis. Conf.})
@String(TOG= {ACM Trans. Graph.})
@String(TIP  = {IEEE Trans. Image Process.})
@String(TVCG  = {IEEE Trans. Vis. Comput. Graph.})
@String(TMM  = {IEEE Trans. Multimedia})
@String(ACMMM= {ACM Int. Conf. Multimedia})
@String(ICME = {Int. Conf. Multimedia and Expo})
@String(ICASSP=	{ICASSP})
@String(ICIP = {IEEE Int. Conf. Image Process.})
@String(ACCV  = {ACCV})
@String(ICLR = {Int. Conf. Learn. Represent.})
@String(IJCAI = {IJCAI})
@String(PR   = {Pattern Recognition})
@String(AAAI = {AAAI})
@String(CVPRW= {IEEE Conf. Comput. Vis. Pattern Recog. Worksh.})
@String(CSVT = {IEEE Trans. Circuit Syst. Video Technol.})

@String(SPL	= {IEEE Sign. Process. Letters})
@String(VR   = {Vis. Res.})
@String(JOV	 = {J. Vis.})
@String(TVC  = {The Vis. Comput.})
@String(JCST  = {J. Comput. Sci. Tech.})
@String(CGF  = {Comput. Graph. Forum})
@String(CVM = {Computational Visual Media})


@String(PAMI  = {IEEE TPAMI})
@String(IJCV  = {IJCV})
@String(CVPR  = {CVPR})
@String(ICCV  = {ICCV})
@String(ECCV  = {ECCV})
@String(NIPS  = {NeurIPS})
@String(ICPR  = {ICPR})
@String(BMVC  =	{BMVC})
@String(TOG   = {ACM TOG})
@String(TIP   = {IEEE TIP})
@String(TVCG  = {IEEE TVCG})
@String(TCSVT = {IEEE TCSVT})
@String(TMM   =	{IEEE TMM})
@String(ACMMM = {ACM MM})
@String(ICME  =	{ICME})
@String(ICASSP=	{ICASSP})
@String(ICIP  = {ICIP})
@String(ACCV  = {ACCV})
@String(ICLR  = {ICLR})
@String(IJCAI = {IJCAI})
@String(PR = {PR})
@String(AAAI = {AAAI})
@String(CVPRW= {CVPRW})
@String(CSVT = {IEEE TCSVT})


@article{pang_deep_2021,
	title = {Deep {Learning} for {Anomaly} {Detection}: {A} {Review}},
	volume = {54},
	issn = {0360-0300},
	shorttitle = {Deep {Learning} for {Anomaly} {Detection}},
	url = {https://dl.acm.org/doi/10.1145/3439950},
	doi = {10.1145/3439950},
	abstract = {Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced approaches. In recent years, deep learning enabled anomaly detection, i.e., deep anomaly detection, has emerged as a critical direction. This article surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in 3 high-level categories and 11 fine-grained categories of the methods. We review their key intuitions, objective functions, underlying assumptions, advantages, and disadvantages and discuss how they address the aforementioned challenges. We further discuss a set of possible future opportunities and new perspectives on addressing the challenges.},
	number = {2},
	urldate = {2025-09-19},
	journal = {ACM Comput. Surv.},
	author = {Pang, Guansong and Shen, Chunhua and Cao, Longbing and Hengel, Anton Van Den},
	month = mar,
	year = {2021},
	pages = {38:1--38:38},
	file = {Full Text PDF:/Users/xz/Zotero/storage/GBT286XM/Pang et al. - 2021 - Deep Learning for Anomaly Detection A Review.pdf:application/pdf},
}


@article{saad_survey_2024,
	title = {A survey on training challenges in generative adversarial networks for biomedical image analysis},
	volume = {57},
	issn = {1573-7462},
	url = {https://doi.org/10.1007/s10462-023-10624-y},
	doi = {10.1007/s10462-023-10624-y},
	abstract = {In biomedical image analysis, the applicability of deep learning methods is directly impacted by the quantity of image data available. This is due to deep learning models requiring large image datasets to provide high-level performance. Generative Adversarial Networks (GANs) have been widely utilized to address data limitations through the generation of synthetic biomedical images. GANs consist of two models. The generator, a model that learns how to produce synthetic images based on the feedback it receives. The discriminator, a model that classifies an image as synthetic or real and provides feedback to the generator. Throughout the training process, a GAN can experience several technical challenges that impede the generation of suitable synthetic imagery. First, the mode collapse problem whereby the generator either produces an identical image or produces a uniform image from distinct input features. Second, the non-convergence problem whereby the gradient descent optimizer fails to reach a Nash equilibrium. Thirdly, the vanishing gradient problem whereby unstable training behavior occurs due to the discriminator achieving optimal classification performance resulting in no meaningful feedback being provided to the generator. These problems result in the production of synthetic imagery that is blurry, unrealistic, and less diverse. To date, there has been no survey article outlining the impact of these technical challenges in the context of the biomedical imagery domain. This work presents a review and taxonomy based on solutions to the training problems of GANs in the biomedical imaging domain. This survey highlights important challenges and outlines future research directions about the training of GANs in the domain of biomedical imagery.},
	language = {en},
	number = {2},
	urldate = {2026-01-25},
	journal = {Artificial Intelligence Review},
	author = {Saad, Muhammad Muneeb and O’Reilly, Ruairi and Rehmani, Mubashir Husain},
	month = jan,
	year = {2024},
	keywords = {Biomedical images, Generative adversarial networks (GANs), Instability, Mode collapse, Non-convergence, Training challenges},
	pages = {19},
	file = {Full Text PDF:/Users/xz/Zotero/storage/ZFQGFRQY/Saad et al. - 2024 - A survey on training challenges in generative adve.pdf:application/pdf},
}

@article{kebaili_deep_2023,
	title = {Deep {Learning} {Approaches} for {Data} {Augmentation} in {Medical} {Imaging}: {A} {Review}},
	volume = {9},
	issn = {2313-433X},
	shorttitle = {Deep {Learning} {Approaches} for {Data} {Augmentation} in {Medical} {Imaging}},
	url = {https://pmc.ncbi.nlm.nih.gov/articles/PMC10144738/},
	doi = {10.3390/jimaging9040081},
	abstract = {Deep learning has become a popular tool for medical image analysis, but the limited availability of training data remains a major challenge, particularly in the medical field where data acquisition can be costly and subject to privacy regulations. Data augmentation techniques offer a solution by artificially increasing the number of training samples, but these techniques often produce limited and unconvincing results. To address this issue, a growing number of studies have proposed the use of deep generative models to generate more realistic and diverse data that conform to the true distribution of the data. In this review, we focus on three types of deep generative models for medical image augmentation: variational autoencoders, generative adversarial networks, and diffusion models. We provide an overview of the current state of the art in each of these models and discuss their potential for use in different downstream tasks in medical imaging, including classification, segmentation, and cross-modal translation. We also evaluate the strengths and limitations of each model and suggest directions for future research in this field. Our goal is to provide a comprehensive review about the use of deep generative models for medical image augmentation and to highlight the potential of these models for improving the performance of deep learning algorithms in medical image analysis.},
	number = {4},
	urldate = {2026-01-25},
	journal = {Journal of Imaging},
	author = {Kebaili, Aghiles and Lapuyade-Lahorgue, Jérôme and Ruan, Su},
	month = apr,
	year = {2023},
	pmid = {37103232},
	pmcid = {PMC10144738},
	pages = {81},
	file = {Full Text:/Users/xz/Zotero/storage/XYD2RCNQ/Kebaili et al. - 2023 - Deep Learning Approaches for Data Augmentation in .pdf:application/pdf},
}

@article{sharma_generative_2024,
	title = {Generative adversarial networks ({GANs}): {Introduction}, {Taxonomy}, {Variants}, {Limitations}, and {Applications}},
	volume = {83},
	issn = {1573-7721},
	shorttitle = {Generative adversarial networks ({GANs})},
	url = {https://doi.org/10.1007/s11042-024-18767-y},
	doi = {10.1007/s11042-024-18767-y},
	abstract = {The growing demand for applications based on Generative Adversarial Networks (GANs) has prompted substantial study and analysis in a variety of fields. GAN models have applications in NLP, architectural design, text-to-image, image-to-image, 3D object production, audio-to-image, and prediction. This technique is an important tool for both production and prediction, notably in identifying falsely created pictures, particularly in the context of face forgeries, to ensure visual integrity and security. GANs are critical in determining visual credibility in social media by identifying and assessing forgeries. As the field progresses, a variety of GAN variations arise, along with the development of diverse assessment techniques for assessing model efficacy and scope. The article provides a complete and exhaustive overview of the most recent advances in GAN model designs, the efficacy and breadth of GAN variations, GAN limits and potential solutions, and the blooming ecosystem of upcoming GAN tool domains. Additionally, it investigates key measures like as Inception Score (IS) and Fréchet Inception Distance (FID) as critical benchmarks for improving GAN performance in contrast to existing approaches.},
	language = {en},
	number = {41},
	urldate = {2026-01-25},
	journal = {Multimedia Tools and Applications},
	author = {Sharma, Preeti and Kumar, Manoj and Sharma, Hitesh Kumar and Biju, Soly Mathew},
	month = dec,
	year = {2024},
	keywords = {Deep fakes, Deep learning, Deep learning based methods, Digital forensics, GAN architecture, GAN models, GAN variants, Generative adversarial network, Image vision},
	pages = {88811--88858},
	file = {Full Text PDF:/Users/xz/Zotero/storage/FBKYLPYU/Sharma et al. - 2024 - Generative adversarial networks (GANs) Introducti.pdf:application/pdf},
}


@article{metheany_characterizing_2008,
	title = {Characterizing anatomical variability in breast {CT} images},
	volume = {35},
	copyright = {© 2008 American Association of Physicists in Medicine},
	issn = {2473-4209},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1118/1.2977772},
	doi = {10.1118/1.2977772},
	abstract = {Previous work [Burgess et al., Med. Phys. 28, 419–437 (2001)] has shown that anatomical noise in projection mammography results in a power spectrum well modeled over a range of frequencies by a power law, and the exponent of this power law plays a critical role in determining the size at which a growing lesion reaches the threshold for detection. In this study, the authors evaluated the power-law model for breast computed tomography (bCT) images, which can be thought of as thin sections through a three-dimensional (3D) volume. Under the assumption of a 3D power law describing the distribution of attenuation coefficients in the breast parenchyma, the authors derived the relationship between the power-law exponents of bCT and projection images and found it to be . They evaluated this relationship on clinical images by comparing bCT images from a set of 43 patients to Burgess’ findings in mammography. They were able to make a direct comparison for 6 of these patients who had both a bCT exam and a digitized film-screen mammogram. They also evaluated segmented bCT images to investigate the extent to which the bCT power-law exponent can be explained by a binary model of attenuation coefficients based on the different attenuation of glandular and adipose tissue. The power-law model was found to be a good fit for bCT data over frequencies from , where anatomical variability dominates the spectrum. The average exponent for bCT images was 1.86. This value is close to the theoretical prediction using Burgess’ published data for projection mammography and for the limited set of mammography data available from the authors’ patient sample. Exponents from the segmented bCT images (average value: 2.06) were systematically slightly higher than bCT images, with substantial correlation between the two .},
	language = {en},
	number = {10},
	urldate = {2026-01-25},
	journal = {Medical Physics},
	author = {Metheany, Kathrine G. and Abbey, Craig K. and Packard, Nathan and Boone, John M.},
	year = {2008},
	note = {\_eprint: https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1118/1.2977772},
	keywords = {biological organs, biological tissues, breast cancer, computed tomography, Computed tomography, computerised tomography, Film mammography, image analysis, mammography, Mammography, Medical image contrast, Medical image noise, Medical image reconstruction, Medical image segmentation, Medical imaging, Medical X-ray imaging, Tissues},
	pages = {4685--4694},
	file = {Full Text PDF:/Users/xz/Zotero/storage/YPVJ4Y3J/Metheany et al. - 2008 - Characterizing anatomical variability in breast CT.pdf:application/pdf;Snapshot:/Users/xz/Zotero/storage/DTF7EFXT/1.html:text/html},
}

@article{engstrom_comparison_2009-1,
	title = {Comparison of power spectra for tomosynthesis projections and reconstructed images},
	volume = {36},
	copyright = {© 2009 American Association of Physicists in Medicine},
	issn = {2473-4209},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1118/1.3116774},
	doi = {10.1118/1.3116774},
	abstract = {Burgess et al. [Med. Phys. 28, 419–437 (2001)] showed that the power spectrum of mammographic breast background follows a power law and that lesion detectability is affected by the power-law exponent which measures the amount of structure in the background. Following the study of Burgess et al., the authors measured and compared the power-law exponent of mammographic backgrounds in tomosynthesis projections and reconstructed slices to investigate the effect of tomosynthesis imaging on background structure. Our data set consisted of 55 patient cases. For each case, regions of interest (ROIs) were extracted from both projection images and reconstructed slices. The periodogram of each ROI was computed by taking the squared modulus of the Fourier transform of the ROI. The power-law exponent was determined for each periodogram and averaged across all ROIs extracted from all projections or reconstructed slices for each patient data set. For the projections, the mean averaged across the 55 cases was 3.06 (standard deviation of 0.21), while it was 2.87 (0.24) for the corresponding reconstructions. The difference in for a given patient between the projection ROIs and the reconstructed ROIs averaged across the 55 cases was 0.194, which was statistically significant . The 95\% CI for the difference between the mean value of for the projections and reconstructions was [0.170, 0.218]. The results are consistent with the observation that the amount of breast structure in the tomosynthesis slice is reduced compared to projection mammography and that this may lead to improved lesion detectability.},
	language = {en},
	number = {5},
	urldate = {2026-01-25},
	journal = {Medical Physics},
	author = {Engstrom, Emma and Reiser, Ingrid and Nishikawa, Robert},
	year = {2009},
	note = {\_eprint: https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1118/1.3116774},
	keywords = {biological organs, Computed tomography, Digital tomosynthesis mammography, Fourier transform spectroscopy, Fourier transforms, image reconstruction, Image reconstruction, mammography, Mammography, Medical image artifacts, Medical image noise, medical image processing, Medical image reconstruction, Medical imaging, Polynomials},
	pages = {1753--1758},
	file = {Full Text PDF:/Users/xz/Zotero/storage/TEUA6YS8/Engstrom et al. - 2009 - Comparison of power spectra for tomosynthesis proj.pdf:application/pdf;Snapshot:/Users/xz/Zotero/storage/M9REL8RH/1.html:text/html},
}


@article{ruff_unifying_2021,
	title = {A {Unifying} {Review} of {Deep} and {Shallow} {Anomaly} {Detection}},
	volume = {109},
	issn = {0018-9219, 1558-2256},
	url = {http://arxiv.org/abs/2009.11732},
	doi = {10.1109/JPROC.2021.3052449},
	abstract = {Deep learning approaches to anomaly detection have recently improved the state of the art in detection performance on complex datasets such as large collections of images or text. These results have sparked a renewed interest in the anomaly detection problem and led to the introduction of a great variety of new methods. With the emergence of numerous such methods, including approaches based on generative models, one-class classification, and reconstruction, there is a growing need to bring methods of this field into a systematic and unified perspective. In this review we aim to identify the common underlying principles as well as the assumptions that are often made implicitly by various methods. In particular, we draw connections between classic 'shallow' and novel deep approaches and show how this relation might cross-fertilize or extend both directions. We further provide an empirical assessment of major existing methods that is enriched by the use of recent explainability techniques, and present specific worked-through examples together with practical advice. Finally, we outline critical open challenges and identify specific paths for future research in anomaly detection.},
	number = {5},
	urldate = {2026-01-25},
	journal = {Proceedings of the IEEE},
	author = {Ruff, Lukas and Kauffmann, Jacob R. and Vandermeulen, Robert A. and Montavon, Grégoire and Samek, Wojciech and Kloft, Marius and Dietterich, Thomas G. and Müller, Klaus-Robert},
	month = may,
	year = {2021},
	note = {arXiv:2009.11732 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning},
	pages = {756--795},
	annote = {Comment: 40 pages; accepted for publication in the Proceedings of the IEEE;},
	file = {Full Text PDF:/Users/xz/Zotero/storage/76UHJ9TZ/Ruff et al. - 2021 - A Unifying Review of Deep and Shallow Anomaly Dete.pdf:application/pdf;Snapshot:/Users/xz/Zotero/storage/6QYW3I8L/2009.html:text/html},
}


@article{wang_mtdiff_2024,
	title = {{MTDiff}: {Visual} anomaly detection with multi-scale diffusion models},
	volume = {302},
	issn = {0950-7051},
	shorttitle = {{MTDiff}},
	url = {https://www.sciencedirect.com/science/article/pii/S0950705124009985},
	doi = {10.1016/j.knosys.2024.112364},
	abstract = {Advancements in computer vision have fueled rapid developments in unsupervised anomaly detection, but current methods often encounter limitations when addressing anomalies with varying scales, and the intricate pipelines that require significant tuning efforts further hinder the usability. In this work, we propose MTDiff, a novel anomaly detection method comprising diffusion models built on different scales. In essence, the constructed scale-specific branches and their incorporation can enhance the pattern coverage, thus improving performance. MTDiff involves two parts: reconstruction that repairs the anomalous region to pseudo-normal, and detection that carefully compares and localizes the anomalies. Instead of the typical forward process of diffusion, we construct a partial Markov chain to improve the reconstruction quality. During the discrimination, we construct a simple but effective detector that operates on feature-level to better utilize the rich contextual information. MTDiff comes with a concise training pipeline, with optimized diffusion iterations ensuring the efficiency. Sufficient experiments reveal that it outperforms the state-of-the-art approaches, showing superior stability and robustness in both image- and pixel-level anomaly detection. The related code is available at https://github.com/vergilben/MTDiff.},
	urldate = {2026-01-25},
	journal = {Knowledge-Based Systems},
	author = {Wang, Xubin and Li, Wenju and He, Xiangjian},
	month = oct,
	year = {2024},
	keywords = {Anomaly detection, Diffusion probabilistic model, Image processing, Pattern recognition, Visual application},
	pages = {112364},
	file = {ScienceDirect Snapshot:/Users/xz/Zotero/storage/MF2KK39X/S0950705124009985.html:text/html},
}


@inproceedings{whang_deblurring_2022,
	title = {Deblurring via {Stochastic} {Refinement}},
	url = {https://ieeexplore.ieee.org/document/9879619/},
	doi = {10.1109/CVPR52688.2022.01581},
	abstract = {Image deblurring is an ill-posed problem with multiple plausible solutions for a given input image. However, most existing methods produce a deterministic estimate of the clean image and are trained to minimize pixel-level distortion. These metrics are known to be poorly correlated with human perception, and often lead to unrealistic reconstructions. We present an alternative framework for blind deblurring based on conditional diffusion models. Unlike existing techniques, we train a stochastic sampler that refines the output of a deterministic predictor and is capable of producing a diverse set of plausible reconstructions for a given input. This leads to a significant improvement in perceptual quality over existing state-of-the-art methods across multiple standard benchmarks. Our predict-and-refine approach also enables much more efficient sampling compared to typical diffusion models. Combined with a carefully tuned network architecture and inference procedure, our method is competitive in terms of distortion metrics such as PSNR. These results show clear benefits of our diffusion-based method for deblurring and challenge the widely used strategy of producing a single, deterministic reconstruction.},
	urldate = {2026-01-25},
	booktitle = {2022 {IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})},
	author = {Whang, Jay and Delbracio, Mauricio and Talebi, Hossein and Saharia, Chitwan and Dimakis, Alexandros G. and Milanfar, Peyman},
	month = jun,
	year = {2022},
	note = {ISSN: 2575-7075},
	keywords = {Computational photography, Computer vision, Distortion, Image and video synthesis and generation, Measurement, Network architecture, Pattern recognition, Predictive models, Stochastic processes},
	pages = {16272--16282},
	file = {Full Text PDF:/Users/xz/Zotero/storage/626RT5Q3/Whang et al. - 2022 - Deblurring via Stochastic Refinement.pdf:application/pdf},
}

@inproceedings{ekmekci_quantifying_2023,
	title = {Quantifying {Generative} {Model} {Uncertainty} in {Posterior} {Sampling} {Methods} for {Computational} {Imaging}},
	url = {https://openreview.net/forum?id=VqFHhTYonI},
	abstract = {The idea of using generative models to perform posterior sampling for imaging inverse problems has elicited attention from the computational imaging community. The main limitation of the existing generative model-based posterior sampling methods is that they do not provide any information about how uncertain the generative model is. In this work, we propose a quick-to-adopt framework that can transform a given generative model-based posterior sampling method into a statistical model that can quantify the generative model uncertainty. The proposed framework is built upon the principles of Bayesian neural networks with latent variables and uses ensembling to capture the uncertainty on the parameters of a generative model. We evaluate the proposed framework on the computed tomography reconstruction problem and demonstrate its capability to quantify generative model uncertainty with an illustrative example. We also show that the proposed method can improve the quality of the reconstructions and the predictive uncertainty estimates of the generative model-based posterior sampling method used within the proposed framework.},
	language = {en},
	urldate = {2026-01-25},
	author = {Ekmekci, Canberk and Cetin, Mujdat},
	month = nov,
	year = {2023},
	file = {Full Text PDF:/Users/xz/Zotero/storage/G4758UV9/Ekmekci and Cetin - 2023 - Quantifying Generative Model Uncertainty in Poster.pdf:application/pdf},
}


@article{he_survey_2025,
	title = {A {Survey} on {Uncertainty} {Quantification} {Methods} for {Deep} {Learning}},
	issn = {0360-0300},
	url = {https://dl.acm.org/doi/10.1145/3786319},
	doi = {10.1145/3786319},
	abstract = {Deep neural networks (DNNs) have achieved tremendous success in computer vision, natural language processing, and scientific and engineering domains. However, DNNs can make unexpected, incorrect, yet overconfident predictions, leading to serious consequences in high-stakes applications such as autonomous driving, medical diagnosis, and disaster response. Uncertainty quantification (UQ) estimates the confidence of DNN predictions in addition to their accuracy. In recent years, many UQ methods have been developed for DNNs. It is valuable to systematically categorize these methods and compare their strengths and limitations. Existing surveys mostly categorize UQ methodologies by neural network architecture or Bayesian formulation, while overlooking the uncertainty sources each method addresses, making it difficult to select an appropriate approach in practice. To fill this gap, this paper presents a taxonomy of UQ methods for DNNs based on uncertainty sources (e.g., data versus model uncertainty). We summarize the advantages and disadvantages of each category, and illustrate how UQ can be applied to machine learning problems (e.g., active learning, out-of-distribution robustness, and deep reinforcement learning). We also identify future research directions, including UQ for large language models (LLMs), AI-driven scientific simulations, and deep neural networks with structured outputs.},
	urldate = {2026-01-25},
	journal = {ACM Comput. Surv.},
	author = {He, Wenchong and Jiang, Zhe and Xiao, Tingsong and Xu, Zelin and Li, Yukun},
	month = dec,
	year = {2025},
	note = {Just Accepted},
	file = {Full Text PDF:/Users/xz/Zotero/storage/GWP7E49H/He et al. - 2025 - A Survey on Uncertainty Quantification Methods for.pdf:application/pdf},
}


@article{chaganti_automated_2020,
	title = {Automated {Quantification} of {CT} {Patterns} {Associated} with {COVID}-19 from {Chest} {CT}},
	volume = {2},
	issn = {2638-6100},
	doi = {10.1148/ryai.2020200048},
	abstract = {PURPOSE: To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities and consolidations.
MATERIALS AND METHODS: In this retrospective study, the proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions, based on a dataset of 9749 chest CT volumes. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities, based on deep learning and deep reinforcement learning. The first measure of (PO, PHO) is global, while the second of (LSS, LHOS) is lobe-wise. Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States collected between 2002-Present (April 2020). Ground truth is established by manual annotations of lesions, lungs, and lobes. Correlation and regression analyses were performed to compare the prediction to the ground truth.
RESULTS: Pearson correlation coefficient between method prediction and ground truth for COVID-19 cases was calculated as 0.92 for PO (P {\textless} .001), 0.97 for PHO (P {\textless} .001), 0.91 for LSS (P {\textless} .001), 0.90 for LHOS (P {\textless} .001). 98 of 100 healthy controls had a predicted PO of less than 1\%, 2 had between 1-2\%. Automated processing time to compute the severity scores was 10 seconds per case compared to 30 minutes required for manual annotations.
CONCLUSION: A new method segments regions of CT abnormalities associated with COVID-19 and computes (PO, PHO), as well as (LSS, LHOS) severity scores.},
	language = {eng},
	number = {4},
	journal = {Radiology. Artificial Intelligence},
	author = {Chaganti, Shikha and Grenier, Philippe and Balachandran, Abishek and Chabin, Guillaume and Cohen, Stuart and Flohr, Thomas and Georgescu, Bogdan and Grbic, Sasa and Liu, Siqi and Mellot, François and Murray, Nicolas and Nicolaou, Savvas and Parker, William and Re, Thomas and Sanelli, Pina and Sauter, Alexander W. and Xu, Zhoubing and Yoo, Youngjin and Ziebandt, Valentin and Comaniciu, Dorin},
	month = jul,
	year = {2020},
	pmid = {33928255},
	pmcid = {PMC7392373},
	pages = {e200048},
	file = {Submitted Version:/Users/xz/Zotero/storage/496CZSZU/Chaganti et al. - 2020 - Automated Quantification of CT Patterns Associated.pdf:application/pdf},
}



@inproceedings{huang_adfa_2023,
	title = {{ADFA}: {Attention}-{Augmented} {Differentiable} {Top}-{K} {Feature} {Adaptation} for {Unsupervised} {Medical} {Anomaly} {Detection}},
	shorttitle = {{ADFA}},
	url = {https://ieeexplore.ieee.org/document/10222528},
	doi = {10.1109/ICIP49359.2023.10222528},
	abstract = {The scarcity of annotated data, particularly for rare diseases, limits the variability of training data and the range of detectable lesions, presenting a significant challenge for supervised anomaly detection in medical imaging. To solve this problem, we propose a novel unsupervised method for medical image anomaly detection: Attention-Augmented Differentiable top-k Feature Adaptation (ADFA). The method utilizes Wide-ResNet50-2 (WR50) network pre-trained on ImageNet to extract initial feature representations. To reduce the channel dimensionality while preserving relevant channel information, we employ an attention-augmented patch descriptor on the extracted features. We then apply differentiable top-k feature adaptation to train the patch descriptor, mapping the extracted feature representations to a new vector space, enabling effective detection of anomalies. Experiments show that ADFA outperforms state-of-the-art (SOTA) methods on multiple challenging medical image datasets, confirming its effectiveness in medical anomaly detection.},
	urldate = {2026-01-25},
	booktitle = {2023 {IEEE} {International} {Conference} on {Image} {Processing} ({ICIP})},
	author = {Huang, Yiming and Liu, Guole and Luo, Yaoru and Yang, Ge},
	month = oct,
	year = {2023},
	keywords = {anomaly detection, Anomaly detection, attention mechanism, Biomedical imaging, feature adaptation, Feature extraction, Image analysis, Lesions, Location awareness, medical image, Training data, unsupervised learning},
	pages = {206--210},
	file = {Snapshot:/Users/xz/Zotero/storage/QWUF2VY2/10222528.html:text/html;Submitted Version:/Users/xz/Zotero/storage/BC3BCV8Q/Huang et al. - 2023 - ADFA Attention-Augmented Differentiable Top-K Fea.pdf:application/pdf},
}

@inproceedings{tschuchnig_anomaly_2022-2,
	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},
	keywords = {anomaly detection, lessons learned, medical imaging},
	pages = {33--38},
	file = {Full Text PDF:/Users/xz/Zotero/storage/C6S63384/Tschuchnig and Gadermayr - 2022 - Anomaly Detection in Medical Imaging - A Mini Revi.pdf:application/pdf},
}

@article{samariya_comprehensive_2023,
	title = {A {Comprehensive} {Survey} of {Anomaly} {Detection} {Algorithms}},
	volume = {10},
	issn = {2198-5812},
	url = {https://doi.org/10.1007/s40745-021-00362-9},
	doi = {10.1007/s40745-021-00362-9},
	abstract = {Anomaly or outlier detection is consider as one of the vital application of data mining, which deals with anomalies or outliers. Anomalies are considered as data points that are dramatically different from the rest of the data points. In this survey, we comprehensively present anomaly detection algorithms in an organized manner. We begin this survey with the definition of anomaly, then provide essential elements of anomaly detection, such as different types of anomaly, different application domains, and evaluation measures. Such anomaly detection algorithms are categorized in seven categories based on their working mechanisms, which includes total of 52 algorithms. The categories are anomaly detection algorithms based on statistics, density, distance, clustering, isolation, ensemble and subspace. For each category, we provide the time complexity of each algorithm and their general advantages and disadvantages. In the end, we compared all discussed anomaly detection algorithms in detail.},
	language = {en},
	number = {3},
	urldate = {2025-09-19},
	journal = {Annals of Data Science},
	author = {Samariya, Durgesh and Thakkar, Amit},
	month = jun,
	year = {2023},
	keywords = {Anomaly, Anomaly detection, Outlier analysis, Outlier detection, Survey},
	pages = {829--850},
	file = {Full Text PDF:/Users/xz/Zotero/storage/J9D3WJR5/Samariya and Thakkar - 2023 - A Comprehensive Survey of Anomaly Detection Algori.pdf:application/pdf},
}

@inproceedings{liu_simplenet_2023,
	title = {{SimpleNet}: {A} {Simple} {Network} for {Image} {Anomaly} {Detection} and {Localization}},
	shorttitle = {{SimpleNet}},
	url = {https://openaccess.thecvf.com/content/CVPR2023/html/Liu_SimpleNet_A_Simple_Network_for_Image_Anomaly_Detection_and_Localization_CVPR_2023_paper.html},
	language = {en},
	urldate = {2025-09-19},
	author = {Liu, Zhikang and Zhou, Yiming and Xu, Yuansheng and Wang, Zilei},
	year = {2023},
	pages = {20402--20411},
	file = {Full Text PDF:/Users/xz/Zotero/storage/MABHXQG5/Liu et al. - 2023 - SimpleNet A Simple Network for Image Anomaly Dete.pdf:application/pdf},
}

@article{xia_gan-based_2022,
	title = {{GAN}-based anomaly detection: {A} review},
	volume = {493},
	issn = {0925-2312},
	shorttitle = {{GAN}-based anomaly detection},
	url = {https://www.sciencedirect.com/science/article/pii/S0925231221019482},
	doi = {10.1016/j.neucom.2021.12.093},
	abstract = {Supervised learning algorithms have shown limited use in the field of anomaly detection due to the unpredictability and difficulty in acquiring abnormal samples. In recent years, unsupervised or semi-supervised anomaly-detection algorithms have become more widely used in anomaly-detection tasks. As a form of unsupervised learning algorithm, generative adversarial networks (GAN/GANs) have been widely used in anomaly detection because GAN can make abnormal inferences using adversarial learning of the representation of samples. To provide inspiration for the research of GAN-based anomaly detection, this review reconsiders the concept of anomaly, provides three criteria for discussing the anomaly detection task, and discusses the current challenges of anomaly detection. For the existing works, this review focuses on the theoretical and technological evolution, theoretical basis, applicable tasks, and practical application of GAN-based anomaly detection. This review also addresses the current internal and external outstanding issues encountered by GAN-based anomaly detection and predicts and analyzes several future research directions in detail. This review summarizes more than 330 references related to GAN-based anomaly detection and provides detailed technical information for researchers who are interested in GANs and want to apply them to anomaly-detection tasks.},
	urldate = {2025-09-19},
	journal = {Neurocomputing},
	author = {Xia, Xuan and Pan, Xizhou and Li, Nan and He, Xing and Ma, Lin and Zhang, Xiaoguang and Ding, Ning},
	month = jul,
	year = {2022},
	keywords = {Adversarial learning and inference, Anomaly detection, Deep learning, Generative adversarial nets, Representation learning},
	pages = {497--535},
	file = {ScienceDirect Snapshot:/Users/xz/Zotero/storage/CM3QSGDV/S0925231221019482.html:text/html},
}

@inproceedings{wolleb_diffusion_2022,
	address = {Cham},
	title = {Diffusion {Models} for {Medical} {Anomaly} {Detection}},
	isbn = {978-3-031-16452-1},
	doi = {10.1007/978-3-031-16452-1_4},
	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.},
	language = {en},
	booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2022},
	publisher = {Springer Nature Switzerland},
	author = {Wolleb, Julia and Bieder, Florentin and Sandkühler, Robin and Cattin, Philippe C.},
	editor = {Wang, Linwei and Dou, Qi and Fletcher, P. Thomas and Speidel, Stefanie and Li, Shuo},
	year = {2022},
	keywords = {Anomaly detection, Diffusion models, Weak supervision},
	pages = {35--45},
	file = {Full Text PDF:/Users/xz/Zotero/storage/FAXN3NJE/Wolleb et al. - 2022 - Diffusion Models for Medical Anomaly Detection.pdf:application/pdf},
}

@article{liu_deep_2024,
	title = {Deep {Industrial} {Image} {Anomaly} {Detection}: {A} {Survey}},
	volume = {21},
	issn = {2731-5398},
	shorttitle = {Deep {Industrial} {Image} {Anomaly} {Detection}},
	url = {https://doi.org/10.1007/s11633-023-1459-z},
	doi = {10.1007/s11633-023-1459-z},
	abstract = {The recent rapid development of deep learning has laid a milestone in industrial image anomaly detection (IAD). In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets. In addition, we extract the promising setting from industrial manufacturing and review the current IAD approaches under our proposed setting. Moreover, we highlight several opening challenges for image anomaly detection. The merits and downsides of representative network architectures under varying supervision are discussed. Finally, we summarize the research findings and point out future research directions. More resources are available at https://github.com/M-3LAB/awesome-industrial-anomaly-detection.},
	language = {en},
	number = {1},
	urldate = {2025-09-19},
	journal = {Machine Intelligence Research},
	author = {Liu, Jiaqi and Xie, Guoyang and Wang, Jinbao and Li, Shangnian and Wang, Chengjie and Zheng, Feng and Jin, Yaochu},
	month = feb,
	year = {2024},
	keywords = {computer vision., deep learning, defect detection, Image anomaly detection, industrial manufacturing},
	pages = {104--135},
	file = {Full Text PDF:/Users/xz/Zotero/storage/N3JIG8PU/Liu et al. - 2024 - Deep Industrial Image Anomaly Detection A Survey.pdf:application/pdf},
}

@inproceedings{basaran_lesionmix_2024,
	address = {Cham},
	title = {{LesionMix}: {A} {Lesion}-{Level} {Data} {Augmentation} {Method} for {Medical} {Image} {Segmentation}},
	isbn = {978-3-031-58171-7},
	shorttitle = {{LesionMix}},
	doi = {10.1007/978-3-031-58171-7_8},
	abstract = {Data augmentation has become a de facto component of deep learning-based medical image segmentation methods. Most data augmentation techniques used in medical imaging focus on spatial and intensity transformations to improve the diversity of training images. They are often designed at the image level, augmenting the full image, and do not pay attention to specific abnormalities within the image. Here, we present LesionMix, a novel and simple lesion-aware data augmentation method. It performs augmentation at the lesion level, increasing the diversity of lesion shape, location, intensity and load distribution, and allowing both lesion populating and inpainting. Experiments on different modalities and different lesion datasets, including four brain MR lesion datasets and one liver CT lesion dataset, demonstrate that LesionMix achieves promising performance in lesion image segmentation, outperforming several recent Mix-based data augmentation methods. The code will be released at https://github.com/dogabasaran/lesionmix.},
	language = {en},
	booktitle = {Data {Augmentation}, {Labelling}, and {Imperfections}},
	publisher = {Springer Nature Switzerland},
	author = {Basaran, Berke Doga and Zhang, Weitong and Qiao, Mengyun and Kainz, Bernhard and Matthews, Paul M. and Bai, Wenjia},
	editor = {Xue, Yuan and Chen, Chen and Chen, Chao and Zuo, Lianrui and Liu, Yihao},
	year = {2024},
	keywords = {Data augmentation, Image synthesis, Lesion image segmentation, Lesion inpainting, Lesion populating},
	pages = {73--83},
	file = {Full Text PDF:/Users/xz/Zotero/storage/TVLBI9I2/Basaran et al. - 2024 - LesionMix A Lesion-Level Data Augmentation Method.pdf:application/pdf},
}

@article{abdelhalim_data_2021,
	title = {Data augmentation for skin lesion using self-attention based progressive generative adversarial network},
	volume = {165},
	issn = {0957-4174},
	url = {https://www.sciencedirect.com/science/article/pii/S0957417420307132},
	doi = {10.1016/j.eswa.2020.113922},
	abstract = {While recent years have witnessed the remarkable success of deep learning methods in automated skin lesion detection systems, there still exists a gap between manual assessment of experts and automated evaluation of computers. The reason behind such a gap is the deep learning models demand considerable amounts of data, while the availability of annotated images is often limited. Data Augmentation (DA) is one way to mitigate the lack of labeled data; however, the augmented images intrinsically have a similar distribution to the original ones, leading to limited performance improvement. To satisfy the data lack in the real image distribution, we synthesize skin lesion images – realistic but completely different from the original ones – using Generative Adversarial Networks (GANs). In this paper, we propose the Self-attention Progressive Growing of GANs (SPGGANs) to generate fine-grained 256 × 256 skin lesion images for Convolutional Neural Network-based melanoma detection, which is challenging via conventional GANs; difficulties arise due to unstable GAN training with high resolution and a variety of skin lesions in size, shape, and location. In SPGGAN, details can be generated using aggregated information from all feature locations. Moreover, the discriminator can monitor that highly detailed features in distant portions of the image are consistent with each other. Furthermore, the Two-Timescale Update Rule (TTUR) is applied to SPGGAN (SPGGAN-TTUR) to improve stability while generating 256 × 256 skin lesion images. SPGGAN-TTUR is evaluated on data generation and classification tasks using the HAM10000 dataset. Our results confirm the importance of our proposed GAN-based DA approach for training skin lesion classifiers and indicate that it can lead to statistically significant improvements (p-value {\textless}0.05) in the sensitivity (recall) over non-augmented and augmented, with classical DA, counterparts. In general, in the case of all classes, The sensitivity improvements were 5.6\% and 2.5\% over non-augmented and augmented (with the best DA scheme) counterparts, respectively. Specifically, in the case of melanoma class, the sensitivity improvements were 13.8\% and 8.6\%. We believe that the proposed approach can be adopted in clinical practice to improve the sensitivity of automated skin lesion detection in dermoscopic images and thus support dermatologists’ efforts to improve melanoma diagnosis.},
	urldate = {2025-09-19},
	journal = {Expert Systems with Applications},
	author = {Abdelhalim, Ibrahim Saad Aly and Mohamed, Mamdouh Farouk and Mahdy, Yousef Bassyouni},
	month = mar,
	year = {2021},
	keywords = {Data augmentation, Data imbalance, Deep learning, Generative models, Skin cancer},
	pages = {113922},
	file = {ScienceDirect Snapshot:/Users/xz/Zotero/storage/398L865M/S0957417420307132.html:text/html;Submitted Version:/Users/xz/Zotero/storage/8XZPP2K3/Abdelhalim et al. - 2021 - Data augmentation for skin lesion using self-atten.pdf:application/pdf},
}

@inproceedings{mok_learning_2019,
	address = {Cham},
	title = {Learning {Data} {Augmentation} for {Brain} {Tumor} {Segmentation} with {Coarse}-to-{Fine} {Generative} {Adversarial} {Networks}},
	isbn = {978-3-030-11723-8},
	doi = {10.1007/978-3-030-11723-8_7},
	abstract = {There is a common belief that the successful training of deep neural networks requires many annotated training samples, which are often expensive and difficult to obtain especially in the biomedical imaging field. While it is often easy for researchers to use data augmentation to expand the size of training sets, constructing and generating generic augmented data that is able to teach the network the desired invariance and robustness properties using traditional data augmentation techniques is challenging in practice. In this paper, we propose a novel automatic data augmentation method that uses generative adversarial networks to learn augmentations that enable machine learning based method to learn the available annotated samples more efficiently. The architecture consists of a coarse-to-fine generator to capture the manifold of the training sets and generate generic augmented data. In our experiments, we show the efficacy of our approach on a Magnetic Resonance Imaging (MRI) image, achieving improvements of 3.5\% Dice coefficient on the BRATS15 Challenge dataset as compared to traditional augmentation approaches. Also, our proposed method successfully boosts a common segmentation network to reach the state-of-the-art performance on the BRATS15 Challenge.},
	language = {en},
	booktitle = {Brainlesion: {Glioma}, {Multiple} {Sclerosis}, {Stroke} and {Traumatic} {Brain} {Injuries}},
	publisher = {Springer International Publishing},
	author = {Mok, Tony C. W. and Chung, Albert C. S.},
	editor = {Crimi, Alessandro and Bakas, Spyridon and Kuijf, Hugo and Keyvan, Farahani and Reyes, Mauricio and van Walsum, Theo},
	year = {2019},
	pages = {70--80},
	file = {Full Text PDF:/Users/xz/Zotero/storage/Z9DZWRZJ/Mok and Chung - 2019 - Learning Data Augmentation for Brain Tumor Segment.pdf:application/pdf},
}

@article{pezeshk_seamless_2017,
	title = {Seamless lesion insertion for data augmentation in {CAD} training},
	volume = {36},
	issn = {0278-0062},
	url = {https://pmc.ncbi.nlm.nih.gov/articles/PMC5509514/},
	doi = {10.1109/TMI.2016.2640180},
	abstract = {The performance of a classifier is largely dependent on the size and representativeness of data used for its training. In circumstances where accumulation and/or labeling of training samples is difficult or expensive, such as medical applications, data augmentation can potentially be used to alleviate the limitations of small datasets. We have previously developed an image blending tool that allows users to modify or supplement an existing CT or mammography dataset by seamlessly inserting a lesion extracted from a source image into a target image. This tool also provides the option to apply various types of transformations to different properties of the lesion prior to its insertion into a new location. In this study, we used this tool to create synthetic samples that appear realistic in chest CT. We then augmented different size training sets with these artificial samples, and investigated the effect of the augmentation on training various classifiers for the detection of lung nodules. Our results indicate that the proposed lesion insertion method can improve classifier performance for small training datasets, and thereby help reduce the need to acquire and label actual patient data.},
	number = {4},
	urldate = {2025-09-19},
	journal = {IEEE transactions on medical imaging},
	author = {Pezeshk, Aria and Petrick, Nicholas and Chen, Weijie and Sahiner, Berkman},
	month = apr,
	year = {2017},
	pmid = {28113310},
	pmcid = {PMC5509514},
	pages = {1005--1015},
	file = {Accepted Version:/Users/xz/Zotero/storage/DFEVFJ4G/Pezeshk et al. - 2017 - Seamless lesion insertion for data augmentation in.pdf:application/pdf},
}

@article{salem_multiple_2019,
	title = {Multiple {Sclerosis} {Lesion} {Synthesis} in {MRI} {Using} an {Encoder}-{Decoder} {U}-{NET}},
	volume = {7},
	issn = {2169-3536},
	url = {https://ieeexplore.ieee.org/document/8645628},
	doi = {10.1109/ACCESS.2019.2900198},
	abstract = {Magnetic resonance imaging (MRI) synthesis has attracted attention due to its various applications in the medical imaging domain. In this paper, we propose generating synthetic multiple sclerosis (MS) lesions on MRI images with the final aim to improve the performance of supervised machine learning algorithms, therefore, avoiding the problem of the lack of available ground truth. We propose a two-input two-output fully convolutional neural network model for MS lesion synthesis in MRI images. The lesion information is encoded as discrete binary intensity level masks passed to the model and stacked with the input images. The model is trained end-to-end without the need for manually annotating the lesions in the training set. We then perform the generation of synthetic lesions on healthy images via registration of patient images, which are subsequently used for data augmentation to increase the performance for supervised MS lesion detection algorithms. Our pipeline is evaluated on MS patient data from an in-house clinical dataset and the public ISBI2015 challenge dataset. The evaluation is based on measuring the similarities between the real and the synthetic images as well as in terms of lesion detection performance by segmenting both the original and synthetic images individually using a state-of-the-art segmentation framework. We also demonstrate the usage of synthetic MS lesions generated on healthy images as data augmentation. We analyze a scenario of limited training data (one-image training) to demonstrate the effect of the data augmentation on both datasets. Our results significantly show the effectiveness of the usage of synthetic MS lesion images. For the ISBI2015 challenge, our one-image model trained using only a single image plus the synthetic data augmentation strategy showed a performance similar to that of other CNN methods that were fully trained using the entire training set, yielding a comparable human expert rater performance.},
	urldate = {2025-09-19},
	journal = {IEEE Access},
	author = {Salem, Mostafa and Valverde, Sergi and Cabezas, Mariano and Pareto, Deborah and Oliver, Arnau and Salvi, Joaquim and Rovira, Àlex and Lladó, Xavier},
	year = {2019},
	keywords = {Brain, convolutional neural network, data augmentation, deep learning, Generators, Image segmentation, Lesions, Magnetic resonance imaging, MRI, multiple sclerosis, Multiple sclerosis, Pipelines, synthetic lesion generation, Training},
	pages = {25171--25184},
	file = {Full Text PDF:/Users/xz/Zotero/storage/JZ73FTYA/Salem et al. - 2019 - Multiple Sclerosis Lesion Synthesis in MRI Using a.pdf:application/pdf},
}

@article{chaganti_automated_2020-1,
	title = {Automated {Quantification} of {CT} {Patterns} {Associated} with {COVID}-19 from {Chest} {CT}},
	volume = {2},
	issn = {2638-6100},
	doi = {10.1148/ryai.2020200048},
	abstract = {PURPOSE: To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities and consolidations.
MATERIALS AND METHODS: In this retrospective study, the proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions, based on a dataset of 9749 chest CT volumes. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities, based on deep learning and deep reinforcement learning. The first measure of (PO, PHO) is global, while the second of (LSS, LHOS) is lobe-wise. Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States collected between 2002-Present (April 2020). Ground truth is established by manual annotations of lesions, lungs, and lobes. Correlation and regression analyses were performed to compare the prediction to the ground truth.
RESULTS: Pearson correlation coefficient between method prediction and ground truth for COVID-19 cases was calculated as 0.92 for PO (P {\textless} .001), 0.97 for PHO (P {\textless} .001), 0.91 for LSS (P {\textless} .001), 0.90 for LHOS (P {\textless} .001). 98 of 100 healthy controls had a predicted PO of less than 1\%, 2 had between 1-2\%. Automated processing time to compute the severity scores was 10 seconds per case compared to 30 minutes required for manual annotations.
CONCLUSION: A new method segments regions of CT abnormalities associated with COVID-19 and computes (PO, PHO), as well as (LSS, LHOS) severity scores.},
	language = {eng},
	number = {4},
	journal = {Radiology. Artificial Intelligence},
	author = {Chaganti, Shikha and Grenier, Philippe and Balachandran, Abishek and Chabin, Guillaume and Cohen, Stuart and Flohr, Thomas and Georgescu, Bogdan and Grbic, Sasa and Liu, Siqi and Mellot, François and Murray, Nicolas and Nicolaou, Savvas and Parker, William and Re, Thomas and Sanelli, Pina and Sauter, Alexander W. and Xu, Zhoubing and Yoo, Youngjin and Ziebandt, Valentin and Comaniciu, Dorin},
	month = jul,
	year = {2020},
	pmid = {33928255},
	pmcid = {PMC7392373},
	pages = {e200048},
	file = {Submitted Version:/Users/xz/Zotero/storage/ZH9D4PIC/Chaganti et al. - 2020 - Automated Quantification of CT Patterns Associated.pdf:application/pdf},
}

@misc{noauthor_automated_nodate,
	title = {Automated quantification of {COVID}-19 severity and progression using chest {CT} images {\textbar} {European} {Radiology}},
	url = {https://link.springer.com/article/10.1007/s00330-020-07156-2},
	urldate = {2025-09-19},
	file = {Automated quantification of COVID-19 severity and progression using chest CT images | European Radiology:/Users/xz/Zotero/storage/NNKDQN5N/s00330-020-07156-2.html:text/html},
}

@article{baratella_interstitial_2021,
	title = {Interstitial {Lung} {Disease} at {High} {Resolution} {CT} after {SARS}-{CoV}-2-{Related} {Acute} {Respiratory} {Distress} {Syndrome} {According} to {Pulmonary} {Segmental} {Anatomy}},
	volume = {10},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2077-0383},
	url = {https://www.mdpi.com/2077-0383/10/17/3985},
	doi = {10.3390/jcm10173985},
	abstract = {Background: The purpose of this study was to evaluate High-Resolution CT (HRCT) findings in SARS-CoV-2-related ARDS survivors treated with prolonged low-dose methylprednisolone after hospital discharge. Methods: A total of 44 consecutive patients (M: 32, F: 12, average age: 64), hospitalised in our department from April to September 2020 for SARS-CoV-2-related ARDS, who had a postdischarge CT scan, were enrolled into this retrospective study. We reviewed the electronic medical charts to collect laboratory, clinical, and demographic data. The CT findings were evaluated and classified according to lung segmental distribution. The imaging findings were correlated with spirometry results and included ground glass opacities (GGOs), consolidations, reticulations, bronchiectasis/bronchiolectasis, linear bands, and loss of pulmonary volume. Results: Alterations in the pulmonary parenchyma were observed in 97.7\% of patients at HRCT (median time lapse between ARDS diagnosis and HRCT: 2.8 months, range 0.9 to 6.7). The most common findings were linear bands (84\%), followed by GGOs (75\%), reticulations (34\%), bronchiolectasis (32\%), consolidations (30\%), bronchiectasis (30\%) and volume loss (25\%). They had a symmetric distribution, and both lower lobes were the most affected areas. Conclusions: A reticular pattern with a posterior distribution was observed 3 months after discharge from severe COVID-19 pneumonia, and this differs from previously described postCOVID-19 fibrotic-like changes. We hypothesized that the systematic use of prolonged low-dose of corticosteroid could be the main reason of this different CT scan appearance.},
	language = {en},
	number = {17},
	urldate = {2025-09-19},
	journal = {Journal of Clinical Medicine},
	author = {Baratella, Elisa and Ruaro, Barbara and Marrocchio, Cristina and Starvaggi, Natalia and Salton, Francesco and Giudici, Fabiola and Quaia, Emilio and Confalonieri, Marco and Cova, Maria Assunta},
	month = jan,
	year = {2021},
	note = {Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {acute respiratory distress syndrome, COVID-19 pneumonia, high resolution computed tomography, pulmonary fibrosis},
	pages = {3985},
	file = {Full Text PDF:/Users/xz/Zotero/storage/YWKF7T8Q/Baratella et al. - 2021 - Interstitial Lung Disease at High Resolution CT af.pdf:application/pdf},
}

@article{ma_high-resolution_2022,
	title = {High-resolution computed tomography features of asbestosis versus fibrotic hypersensitivity pneumonitis: an observational study},
	volume = {22},
	issn = {1471-2466},
	shorttitle = {High-resolution computed tomography features of asbestosis versus fibrotic hypersensitivity pneumonitis},
	url = {https://doi.org/10.1186/s12890-022-01967-3},
	doi = {10.1186/s12890-022-01967-3},
	abstract = {Asbestosis and fibrotic hypersensitivity pneumonitis (FHP) share the pathogenetic mechanisms induced bronchiolocentric fibrotic process secondary to inhalation exposure. Under the occupational and environmental mixed exposures, asbestosis and FHP are needed to make the differential diagnoses on high-resolution computed tomography (HRCT), especially in the countries still using asbestos. The study aimed to analyze the HRCT features of asbestosis versus FHP.},
	language = {en},
	number = {1},
	urldate = {2025-09-19},
	journal = {BMC Pulmonary Medicine},
	author = {Ma, Ruimin and Li, Shuang and Wang, Yuanying and Yang, Shuqiao and Bao, Na and Ye, Qiao},
	month = may,
	year = {2022},
	keywords = {Asbestosis, Fibrosis, High-resolution computed tomography, Hypersensitivity pneumonitis, Pleura},
	pages = {207},
	file = {Full Text PDF:/Users/xz/Zotero/storage/XQZVK8YY/Ma et al. - 2022 - High-resolution computed tomography features of as.pdf:application/pdf},
}

@article{akira_high-resolution_2003,
	title = {High-resolution {CT} of asbestosis and idiopathic pulmonary fibrosis},
	volume = {181},
	issn = {0361-803X},
	doi = {10.2214/ajr.181.1.1810163},
	abstract = {OBJECTIVE: We studied high-resolution CT of asbestosis and idiopathic pulmonary fibrosis to determine whether differences-other than the frequency of associated pleural changes-could be discerned between the two diseases.
MATERIALS AND METHODS: High-resolution CT scans of 80 patients with asbestosis and 80 patients with idiopathic pulmonary fibrosis were retrospectively reviewed. Two chest radiologists assessed the type and distribution of parenchymal and pleural abnormalities on high-resolution CT.
RESULTS: Subpleural dotlike or branching opacities (65/80), subpleural curvilinear lines (55/80), mosaic perfusion (39/80), and parenchymal bands (38/80) were more common in patients with asbestosis (p {\textless} 0.0001). Visible intralobular bronchioles (62/80), bronchiolectasis within fibrotic consolidations (47/80), and honeycombing (61/80) were more common in patients with idiopathic pulmonary fibrosis (p {\textless} 0.0001). The frequencies of interlobular septal thickening, ground-glass opacities, fibrotic consolidation, and emphysema were similar in both groups. Parenchymal bands and fibrotic consolidation were more commonly seen (p {\textless} 0. 05) in patients with asbestosis associated with pleural disease (n = 66) than in patients with asbestosis without pleural disease (n = 14). Also, statistically significant differences were noted between high-resolution CT findings of patients with asbestosis without pleural disease and those of patients with idiopathic pulmonary fibrosis, except for parenchymal bands.
CONCLUSION: Specific combinations of high-resolution CT findings strongly suggest either asbestosis or idiopathic pulmonary fibrosis. We found that CT findings that might have represented bronchiolar obstruction in the subpleural region were more prominent in patients with asbestosis than in those with idiopathic pulmonary fibrosis, whereas bronchiolar dilatation was more prominent in patients with idiopathic pulmonary fibrosis than in those with asbestosis.},
	language = {eng},
	number = {1},
	journal = {AJR. American journal of roentgenology},
	author = {Akira, Masanori and Yamamoto, Satoru and Inoue, Yoshikazu and Sakatani, Mitsunori},
	month = jul,
	year = {2003},
	pmid = {12818850},
	keywords = {Asbestosis, Diagnosis, Differential, Female, Humans, Lung, Male, Middle Aged, Pulmonary Fibrosis, Retrospective Studies, Tomography, X-Ray Computed},
	pages = {163--169},
}

@article{brixey_pictorial_2024,
	title = {Pictorial {Review} of {Fibrotic} {Interstitial} {Lung} {Disease} on {High}-{Resolution} {CT} {Scan} and {Updated} {Classification}},
	volume = {165},
	issn = {1931-3543},
	doi = {10.1016/j.chest.2023.11.037},
	abstract = {TOPIC IMPORTANCE: Given the recently expanded approval of antifibrotics for various fibrotic interstitial lung diseases (ILDs), early and correct recognition of these diseases is imperative for physicians. Because high-resolution chest CT scan forms the backbone of diagnosis for ILD, this review will discuss evidence-based imaging findings of key fibrotic ILDs and an approach for differentiating these diseases.
REVIEW FINDINGS: (1) Imaging findings of nonspecific interstitial pneumonia may evolve over time and become indistinguishable from usual interstitial pneumonia. Therefore, if remote imaging can be reviewed, this would increase the likelihood of an accurate imaging diagnosis, particularly if findings appear to represent a usual interstitial pneumonia pattern on the recent examination. (2) Given the difficulty and lack of objectivity in classifying patients with hypersensitivity pneumonitis into acute, subacute, and chronic categories and that prognosis depends primarily on presence or absence of fibrosis, the new set of guidelines released in 2020 categorizes patients with hypersensitivity pneumonitis as either nonfibrotic (purely inflammatory) or fibrotic (either purely fibrotic or mixed fibrotic/inflammatory) based on imaging and/or histologic findings, and the prior temporal terms are no longer used. (3) Interstitial lung abnormalities are incidental CT scan findings that may suggest early ILD in patients without clinical suspicion for ILD. Patients with high-risk features should undergo clinical evaluation for ILD and be actively monitored for disease progression.
SUMMARY: Fibrotic ILD on high-resolution chest CT scan is a complex topic, but with use of an evidence-based analysis and algorithm as provided in this article, the probability of a correct imaging diagnosis increases.},
	language = {eng},
	number = {4},
	journal = {Chest},
	author = {Brixey, Anupama Gupta and Oh, Andrea S. and Alsamarraie, Aseel and Chung, Jonathan H.},
	month = apr,
	year = {2024},
	pmid = {38056824},
	keywords = {Alveolitis, Extrinsic Allergic, fibrosing lung disease, Fibrosis, fibrotic HP, fibrotic lung disease, HP, Humans, Idiopathic Pulmonary Fibrosis, ILA, interstitial lung disease, Lung, Lung Diseases, Interstitial, NSIP, PPFE, Tomography, X-Ray Computed, UIP},
	pages = {908--923},
}

@inproceedings{akcay_ganomaly_2019,
	address = {Cham},
	title = {{GANomaly}: {Semi}-supervised {Anomaly} {Detection} via {Adversarial} {Training}},
	isbn = {978-3-030-20893-6},
	shorttitle = {{GANomaly}},
	doi = {10.1007/978-3-030-20893-6_39},
	abstract = {Anomaly detection is a classical problem in computer vision, namely the determination of the normal from the abnormal when datasets are highly biased towards one class (normal) due to the insufficient sample size of the other class (abnormal). While this can be addressed as a supervised learning problem, a significantly more challenging problem is that of detecting the unknown/unseen anomaly case that takes us instead into the space of a one-class, semi-supervised learning paradigm. We introduce such a novel anomaly detection model, by using a conditional generative adversarial network that jointly learns the generation of high-dimensional image space and the inference of latent space. Employing encoder-decoder-encoder sub-networks in the generator network enables the model to map the input image to a lower dimension vector, which is then used to reconstruct the generated output image. The use of the additional encoder network maps this generated image to its latent representation. Minimizing the distance between these images and the latent vectors during training aids in learning the data distribution for the normal samples. As a result, a larger distance metric from this learned data distribution at inference time is indicative of an outlier from that distribution—an anomaly. Experimentation over several benchmark datasets, from varying domains, shows the model efficacy and superiority over previous state-of-the-art approaches.},
	language = {en},
	booktitle = {Computer {Vision} – {ACCV} 2018},
	publisher = {Springer International Publishing},
	author = {Akcay, Samet and Atapour-Abarghouei, Amir and Breckon, Toby P.},
	editor = {Jawahar, C. V. and Li, Hongdong and Mori, Greg and Schindler, Konrad},
	year = {2019},
	keywords = {Anomaly detection, Generative Adversarial Networks, Semi-supervised learning, X-ray security imagery},
	pages = {622--637},
	file = {Full Text PDF:/Users/xz/Zotero/storage/WHTQBSLD/Akcay et al. - 2019 - GANomaly Semi-supervised Anomaly Detection via Ad.pdf:application/pdf},
}

@inproceedings{ho_denoising_2020,
	title = {Denoising {Diffusion} {Probabilistic} {Models}},
	volume = {33},
	url = {https://proceedings.neurips.cc/paper/2020/hash/4c5bcfec8584af0d967f1ab10179ca4b-Abstract.html},
	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.},
	urldate = {2025-09-19},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher = {Curran Associates, Inc.},
	author = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
	year = {2020},
	pages = {6840--6851},
	file = {Full Text PDF:/Users/xz/Zotero/storage/GCWSB5CY/Ho et al. - 2020 - Denoising Diffusion Probabilistic Models.pdf:application/pdf},
}

@inproceedings{wyatt_anoddpm_2022,
	title = {{AnoDDPM}: {Anomaly} {Detection} with {Denoising} {Diffusion} {Probabilistic} {Models} using {Simplex} {Noise}},
	shorttitle = {{AnoDDPM}},
	url = {https://ieeexplore.ieee.org/document/9857019},
	doi = {10.1109/CVPRW56347.2022.00080},
	abstract = {Generative models have been shown to provide a powerful mechanism for anomaly detection by learning to model healthy or normal reference data which can subsequently be used as a baseline for scoring anomalies. In this work we consider denoising diffusion probabilistic models (DDPMs) for unsupervised anomaly detection. DDPMs have superior mode coverage over generative adversarial networks (GANs) and higher sample quality than variational autoencoders (VAEs). However, this comes at the expense of poor scalability and increased sampling times due to the long Markov chain sequences required. We observe that within reconstruction-based anomaly detection a full-length Markov chain diffusion is not required. This leads us to develop a novel partial diffusion anomaly detection strategy that scales to high-resolution imagery, named AnoDDPM. A secondary problem is that Gaussian diffusion fails to capture larger anomalies; therefore we develop a multi-scale simplex noise diffusion process that gives control over the target anomaly size. AnoDDPM with simplex noise is shown to significantly outperform both f-AnoGAN and Gaussian diffusion for the tumorous dataset of 22 T1-weighted MRI scans (CCBS Edinburgh) qualitatively and quantitatively (improvement of +25.5\% Sørensen–Dice coefficient, +17.6\% IoU and +7.4\% AUC).},
	urldate = {2025-09-19},
	booktitle = {2022 {IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} {Workshops} ({CVPRW})},
	author = {Wyatt, Julian and Leach, Adam and Schmon, Sebastian M. and Willcocks, Chris G.},
	month = jun,
	year = {2022},
	note = {ISSN: 2160-7516},
	keywords = {Gaussian noise, Image segmentation, Magnetic resonance imaging, Markov processes, Noise reduction, Shape, Training},
	pages = {649--655},
	file = {Accepted Version:/Users/xz/Zotero/storage/9UW6LQR8/Wyatt et al. - 2022 - AnoDDPM Anomaly Detection with Denoising Diffusio.pdf:application/pdf;Snapshot:/Users/xz/Zotero/storage/6VIJQ2GV/9857019.html:text/html},
}

@article{schlegl_f-anogan_2019,
	title = {f-{AnoGAN}: {Fast} unsupervised anomaly detection with generative adversarial networks},
	volume = {54},
	issn = {1361-8415},
	shorttitle = {f-{AnoGAN}},
	url = {https://www.sciencedirect.com/science/article/pii/S1361841518302640},
	doi = {10.1016/j.media.2019.01.010},
	abstract = {Obtaining expert labels in clinical imaging is difficult since exhaustive annotation is time-consuming. Furthermore, not all possibly relevant markers may be known and sufficiently well described a priori to even guide annotation. While supervised learning yields good results if expert labeled training data is available, the visual variability, and thus the vocabulary of findings, we can detect and exploit, is limited to the annotated lesions. Here, we present fast AnoGAN (f-AnoGAN), a generative adversarial network (GAN) based unsupervised learning approach capable of identifying anomalous images and image segments, that can serve as imaging biomarker candidates. We build a generative model of healthy training data, and propose and evaluate a fast mapping technique of new data to the GAN’s latent space. The mapping is based on a trained encoder, and anomalies are detected via a combined anomaly score based on the building blocks of the trained model – comprising a discriminator feature residual error and an image reconstruction error. In the experiments on optical coherence tomography data, we compare the proposed method with alternative approaches, and provide comprehensive empirical evidence that f-AnoGAN outperforms alternative approaches and yields high anomaly detection accuracy. In addition, a visual Turing test with two retina experts showed that the generated images are indistinguishable from real normal retinal OCT images. The f-AnoGAN code is available at https://github.com/tSchlegl/f-AnoGAN.},
	urldate = {2025-09-19},
	journal = {Medical Image Analysis},
	author = {Schlegl, Thomas and Seeböck, Philipp and Waldstein, Sebastian M. and Langs, Georg and Schmidt-Erfurth, Ursula},
	month = may,
	year = {2019},
	keywords = {Anomaly detection, Optical coherence tomography, Unsupervised learning, Wasserstein generative adversarial network},
	pages = {30--44},
	file = {ScienceDirect Snapshot:/Users/xz/Zotero/storage/KKRSS6DV/S1361841518302640.html:text/html},
}

@article{gaillandre_ultra-high_2023,
	title = {Ultra-high resolution {CT} imaging of interstitial lung disease: impact of photon-counting {CT} in 112 patients},
	volume = {33},
	issn = {1432-1084},
	shorttitle = {Ultra-high resolution {CT} imaging of interstitial lung disease},
	doi = {10.1007/s00330-023-09616-x},
	abstract = {OBJECTIVES: To compare lung parenchyma analysis on ultra-high resolution (UHR) images of a photon-counting CT (PCCT) scanner with that of high-resolution (HR) images of an energy-integrating detector CT (EID-CT).
METHODS: A total of 112 patients with stable interstitial lung disease (ILD) were investigated (a) at T0 with HRCT on a 3rd-generation dual-source CT scanner; (b) at T1 with UHR on a PCCT scanner; (c) with a comparison of 1-mm-thick lung images.
RESULTS: Despite a higher level of objective noise at T1 (74.1 ± 14.1 UH vs 38.1 ± 8.7 UH; p {\textless} 0.0001), higher qualitative scores were observed at T1 with (a) visualization of more distal bronchial divisions (median order; Q1-Q3) (T1: 10th division [9-10]; T0: 9th division [8-9]; p {\textless} 0.0001); (b) greater scores of sharpness of bronchial walls (p {\textless} 0.0001) and right major fissure (p {\textless} 0.0001). The scores of visualization of CT features of ILD were significantly superior at T1 (micronodules: p = 0.03; linear opacities, intralobular reticulation, bronchiectasis, bronchiolectasis, and honeycombing: p {\textless} 0.0001), leading to the reclassification of 4 patients with non-fibrotic ILD at T0, recognized with fibrotic ILD at T1. At T1, the mean (± SD) radiation dose (CTDI vol: 2.7 ± 0.5 mGy; DLP: 88.5 ± 21 mGy.cm) was significantly lower than that delivered at T0 (CTDI vol: 3.6 ± 0.9 mGy; DLP: 129.8 ± 31.7 mGy.cm) (p {\textless} 0.0001), corresponding to a mean reduction of 27\% and 32\% for the CTDIvol and DLP, respectively.
CONCLUSIONS: The UHR scanning mode of PCCT allowed a more precise depiction of CT features of ILDs and reclassification of ILD patterns with significant radiation dose reduction.
CLINICAL RELEVANCE STATEMENT: Evaluation of lung parenchymal structures with ultra-high-resolution makes subtle changes at the level of the secondary pulmonary lobules and lung microcirculation becoming visually accessible, opening new options for synergistic collaborations between highly-detailed morphology and artificial intelligence.
KEY POINTS: • Photon-counting CT (PCCT) provides a more precise analysis of lung parenchymal structures and CT features of interstitial lung diseases (ILDs). • The UHR mode ensures a more precise delineation of fine fibrotic abnormalities with the potential of modifying the categorization of ILD patterns. • Better image quality at a lower radiation dose with PCCT opens new horizons for further dose reduction in noncontrast UHR examinations.},
	language = {eng},
	number = {8},
	journal = {European Radiology},
	author = {Gaillandre, Yann and Duhamel, Alain and Flohr, Thomas and Faivre, Jean-Baptiste and Khung, Suonita and Hutt, Antoine and Felloni, Paul and Remy, Jacques and Remy-Jardin, Martine},
	month = aug,
	year = {2023},
	pmid = {37071165},
	keywords = {Artificial Intelligence, Computed tomography, Humans, Lung, Lung diseases, interstitial, Lung Diseases, Interstitial, Photons, Thorax, Tomography, X-Ray Computed},
	pages = {5528--5539},
}

@article{pu_automated_2021,
	title = {Automated quantification of {COVID}-19 severity and progression using chest {CT} images},
	volume = {31},
	issn = {1432-1084},
	doi = {10.1007/s00330-020-07156-2},
	abstract = {OBJECTIVE: To develop and test computer software to detect, quantify, and monitor progression of pneumonia associated with COVID-19 using chest CT scans.
METHODS: One hundred twenty chest CT scans from subjects with lung infiltrates were used for training deep learning algorithms to segment lung regions and vessels. Seventy-two serial scans from 24 COVID-19 subjects were used to develop and test algorithms to detect and quantify the presence and progression of infiltrates associated with COVID-19. The algorithm included (1) automated lung boundary and vessel segmentation, (2) registration of the lung boundary between serial scans, (3) computerized identification of the pneumonitis regions, and (4) assessment of disease progression. Agreement between radiologist manually delineated regions and computer-detected regions was assessed using the Dice coefficient. Serial scans were registered and used to generate a heatmap visualizing the change between scans. Two radiologists, using a five-point Likert scale, subjectively rated heatmap accuracy in representing progression.
RESULTS: There was strong agreement between computer detection and the manual delineation of pneumonic regions with a Dice coefficient of 81\% (CI 76-86\%). In detecting large pneumonia regions ({\textgreater} 200 mm3), the algorithm had a sensitivity of 95\% (CI 94-97\%) and specificity of 84\% (CI 81-86\%). Radiologists rated 95\% (CI 72 to 99) of heatmaps at least "acceptable" for representing disease progression.
CONCLUSION: The preliminary results suggested the feasibility of using computer software to detect and quantify pneumonic regions associated with COVID-19 and to generate heatmaps that can be used to visualize and assess progression.
KEY POINTS: • Both computer vision and deep learning technology were used to develop computer software to quantify the presence and progression of pneumonia associated with COVID-19 depicted on CT images. • The computer software was tested using both quantitative experiments and subjective assessment. • The computer software has the potential to assist in the detection of the pneumonic regions, monitor disease progression, and assess treatment efficacy related to COVID-19.},
	language = {eng},
	number = {1},
	journal = {European Radiology},
	author = {Pu, Jiantao and Leader, Joseph K. and Bandos, Andriy and Ke, Shi and Wang, Jing and Shi, Junli and Du, Pang and Guo, Youmin and Wenzel, Sally E. and Fuhrman, Carl R. and Wilson, David O. and Sciurba, Frank C. and Jin, Chenwang},
	month = jan,
	year = {2021},
	pmid = {32789756},
	pmcid = {PMC7755837},
	keywords = {Adult, Algorithms, Biomarkers, COVID-19, Deep Learning, Disease Progression, Humans, Lung, Middle Aged, Neural network, Pneumonia, Retrospective Studies, SARS-CoV-2, Software, Tomography, X-Ray Computed},
	pages = {436--446},
	file = {Full Text:/Users/xz/Zotero/storage/2YWWUR66/Pu et al. - 2021 - Automated quantification of COVID-19 severity and .pdf:application/pdf},
}

@article{ruderman_origins_1997,
	title = {Origins of scaling in natural images},
	volume = {37},
	issn = {0042-6989},
	url = {https://www.sciencedirect.com/science/article/pii/S0042698997000084},
	doi = {10.1016/S0042-6989(97)00008-4},
	abstract = {One of the most robust qualities of our visual world is the scale invariance of natural images. Not only has scaling been found in different visual environments, but the phenomenon also appears to be calibration-independent. This paper proposes a simple property of natural images which explains this robustness they are collages of regions corresponding to statistically independent “objects”. Evidence is provided for these objects having a power-law distribution of sizes within images, from which follows scaling in natural images. It is commonly suggested that scaling instead results from edges, each with power spectrum 1k/2. This hypothesis is refuted by example.},
	number = {23},
	urldate = {2025-09-19},
	journal = {Vision Research},
	author = {Ruderman, Daniel L.},
	month = dec,
	year = {1997},
	keywords = {Natural images, Power spectrum, Scaling},
	pages = {3385--3398},
	file = {ScienceDirect Snapshot:/Users/xz/Zotero/storage/BVDLX3UF/S0042698997000084.html:text/html},
}

@inproceedings{dhariwal_diffusion_2021,
	title = {Diffusion {Models} {Beat} {GANs} on {Image} {Synthesis}},
	url = {https://openreview.net/forum?id=AAWuCvzaVt},
	abstract = {We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations. For conditional image synthesis, we further improve sample quality with classifier guidance: a simple, compute-efficient method for trading off diversity for fidelity using gradients from a classifier. We achieve an FID of 2.97 on ImageNet 128\${\textbackslash}times\$128, 4.59 on ImageNet 256\${\textbackslash}times\$256, and 7.72 on ImageNet 512\${\textbackslash}times\$512, and we match BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution. Finally, we find that classifier guidance combines well with upsampling diffusion models, further improving FID to 3.94 on ImageNet 256\${\textbackslash}times\$256 and 3.85 on ImageNet 512\${\textbackslash}times\$512.},
	language = {en},
	urldate = {2025-09-19},
	author = {Dhariwal, Prafulla and Nichol, Alexander Quinn},
	month = nov,
	year = {2021},
	file = {Full Text PDF:/Users/xz/Zotero/storage/B7LCYUWN/Dhariwal and Nichol - 2021 - Diffusion Models Beat GANs on Image Synthesis.pdf:application/pdf},
}

@article{nichol_improved_2020,
	title = {Improved {Denoising} {Diffusion} {Probabilistic} {Models}},
	url = {https://openreview.net/forum?id=-NEXDKk8gZ},
	abstract = {We explore denoising diffusion probabilistic models, a class of generative models which have recently been shown to produce excellent samples in the image and audio domains. While these models produce excellent samples, it has yet to be shown that they can achieve competitive log-likelihoods. We show that, with several small modifications, diffusion models can achieve competitive log-likelihoods in the image domain while maintaining high sample quality. Additionally, our models allow for sampling with an order of magnitude fewer diffusion steps with only a modest difference in sample quality. Finally, we explore how sample quality and log-likelihood scale with the number of diffusion steps and the amount of model capacity. We conclude that denoising diffusion probabilistic models are a promising class of generative models with excellent scaling properties and sample quality.},
	language = {en},
	urldate = {2025-09-19},
	author = {Nichol, Alexander Quinn and Dhariwal, Prafulla},
	month = oct,
	year = {2020},
	file = {Full Text PDF:/Users/xz/Zotero/storage/QVWN6BJE/Nichol and Dhariwal - 2020 - Improved Denoising Diffusion Probabilistic Models.pdf:application/pdf},
}

@article{zimmerer_context-encoding_2019,
	title = {Context-encoding {Variational} {Autoencoder} for {Unsupervised} {Anomaly} {Detection}},
	url = {https://openreview.net/forum?id=BylLiVXptV},
	abstract = {Unsupervised learning can leverage large-scale data sources without the need for annotations. In this context, deep learning-based autoencoders have shown great potential in detecting anomalies in medical images. However, especially Variational Autoencoders (VAEs) often fail to capture the high-level structure in the data. We address these shortcomings by proposing the context-encoding Variational Autoencoder (ceVAE), which improves both, the sample- as well as pixelwise results. In our experiments on the BraTS-2017 and ISLES2015 segmentation benchmarks, the ceVAE achieves unsupervised AUROCs of 0.95 and 0.89, respectively, thus outperforming other reported deep-learning based approaches.},
	language = {en},
	urldate = {2025-09-19},
	author = {Zimmerer, David and Kohl, Simon and Petersen, Jens and Isensee, Fabian and Maier-Hein, Klaus},
	month = apr,
	year = {2019},
	file = {Full Text PDF:/Users/xz/Zotero/storage/J9VXHLM6/Zimmerer et al. - 2019 - Context-encoding Variational Autoencoder for Unsup.pdf:application/pdf},
}

@inproceedings{zhou_anomaly_2017,
	address = {New York, NY, USA},
	series = {{KDD} '17},
	title = {Anomaly {Detection} with {Robust} {Deep} {Autoencoders}},
	isbn = {978-1-4503-4887-4},
	url = {https://doi.org/10.1145/3097983.3098052},
	doi = {10.1145/3097983.3098052},
	abstract = {Deep autoencoders, and other deep neural networks, have demonstrated their effectiveness in discovering non-linear features across many problem domains. However, in many real-world problems, large outliers and pervasive noise are commonplace, and one may not have access to clean training data as required by standard deep denoising autoencoders. Herein, we demonstrate novel extensions to deep autoencoders which not only maintain a deep autoencoders' ability to discover high quality, non-linear features but can also eliminate outliers and noise without access to any clean training data. Our model is inspired by Robust Principal Component Analysis, and we split the input data X into two parts, \$X = L\_\{D\} + S\$, where \$L\_\{D\}\$ can be effectively reconstructed by a deep autoencoder and \$S\$ contains the outliers and noise in the original data X. Since such splitting increases the robustness of standard deep autoencoders, we name our model a "Robust Deep Autoencoder (RDA)". Further, we present generalizations of our results to grouped sparsity norms which allow one to distinguish random anomalies from other types of structured corruptions, such as a collection of features being corrupted across many instances or a collection of instances having more corruptions than their fellows. Such "Group Robust Deep Autoencoders (GRDA)" give rise to novel anomaly detection approaches whose superior performance we demonstrate on a selection of benchmark problems.},
	urldate = {2025-09-19},
	booktitle = {Proceedings of the 23rd {ACM} {SIGKDD} {International} {Conference} on {Knowledge} {Discovery} and {Data} {Mining}},
	publisher = {Association for Computing Machinery},
	author = {Zhou, Chong and Paffenroth, Randy C.},
	month = aug,
	year = {2017},
	pages = {665--674},
}

@inproceedings{zhang_unsupervised_2023,
	title = {Unsupervised {Surface} {Anomaly} {Detection} with {Diffusion} {Probabilistic} {Model}},
	url = {https://ieeexplore.ieee.org/document/10377534},
	doi = {10.1109/ICCV51070.2023.00624},
	abstract = {Unsupervised surface anomaly detection aims at discovering and localizing anomalous patterns using only anomaly-free training samples. Reconstruction-based models are among the most popular and successful methods, which rely on the assumption that anomaly regions are more difficult to reconstruct. However, there are three major challenges to the practical application of this approach: 1) the reconstruction quality needs to be further improved since it has a great impact on the final result, especially for images with structural changes; 2) it is observed that for many neural networks, the anomalies can also be well reconstructed, which severely violates the underlying assumption; 3) since reconstruction is an ill-conditioned problem, a test instance may correspond to multiple normal patterns, but most current reconstruction-based methods have ignored this critical fact. In this paper, we propose DiffAD, a method for unsupervised anomaly detection based on the latent diffusion model, inspired by its ability to generate high-quality and diverse images. We further propose noisy condition embedding and interpolated channels to address the aforementioned challenges in the general reconstruction-based pipeline. Extensive experiments show that our method achieves state-of-the-art performance on the challenging MVTec dataset, especially in localization accuracy.},
	urldate = {2025-09-19},
	booktitle = {2023 {IEEE}/{CVF} {International} {Conference} on {Computer} {Vision} ({ICCV})},
	author = {Zhang, Xinyi and Li, Naiqi and Li, Jiawei and Dai, Tao and Jiang, Yong and Xia, Shu-Tao},
	month = oct,
	year = {2023},
	note = {ISSN: 2380-7504},
	keywords = {Deformation, Location awareness, Neural networks, Pipelines, Probabilistic logic, Surface reconstruction, Training},
	pages = {6759--6768},
	file = {Snapshot:/Users/xz/Zotero/storage/P5BBHDLS/10377534.html:text/html},
}

@inproceedings{he_diffusion-based_2024,
	series = {{AAAI}'24/{IAAI}'24/{EAAI}'24},
	title = {A diffusion-based framework for multi-class anomaly detection},
	volume = {38},
	isbn = {978-1-57735-887-9},
	url = {https://doi.org/10.1609/aaai.v38i8.28690},
	doi = {10.1609/aaai.v38i8.28690},
	abstract = {Reconstruction-based approaches have achieved remarkable outcomes in anomaly detection. The exceptional image reconstruction capabilities of recently popular diffusion models have sparked research efforts to utilize them for enhanced reconstruction of anomalous images. Nonetheless, these methods might face challenges related to the preservation of image categories and pixel-wise structural integrity in the more practical multi-class setting. To solve the above problems, we propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection, which consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion's denoising network, and a feature-space pre-trained feature extractor. Firstly, The SG network is proposed for reconstructing anomalous regions while preserving the original image's semantic information. Secondly, we introduce Spatial-aware Feature Fusion (SFF) block to maximize reconstruction accuracy when dealing with extensively reconstructed areas. Thirdly, the input and reconstructed images are processed by a pre-trained feature extractor to generate anomaly maps based on features extracted at different scales. Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach which surpasses the state-of-the-art methods, e.g., achieving 96.8/52.6 and 97.2/99.0 (AUROC/AP) for localization and detection respectively on multi-class MVTec-AD dataset. Code is available at https://lewandofskee.github.io/projects/diad.},
	urldate = {2025-09-19},
	booktitle = {Proceedings of the {Thirty}-{Eighth} {AAAI} {Conference} on {Artificial} {Intelligence} and {Thirty}-{Sixth} {Conference} on {Innovative} {Applications} of {Artificial} {Intelligence} and {Fourteenth} {Symposium} on {Educational} {Advances} in {Artificial} {Intelligence}},
	publisher = {AAAI Press},
	author = {He, Haoyang and Zhang, Jiangning and Chen, Hongxu and Chen, Xuhai and Li, Zhishan and Chen, Xu and Wang, Yabiao and Wang, Chengjie and Xie, Lei},
	month = feb,
	year = {2024},
	pages = {8472--8480},
	file = {Full Text:/Users/xz/Zotero/storage/RNRU66XJ/He et al. - 2024 - A diffusion-based framework for multi-class anomal.pdf:application/pdf},
}
@InProceedings{Ber_Diffusion_MICCAI2024,
        author = { Bercea, Cosmin I. and Wiestler, Benedikt and Rueckert, Daniel and Schnabel, Julia A.},
        title = { { Diffusion Models with Implicit Guidance for Medical Anomaly Detection } },
        booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
        year = {2024},
        publisher = {Springer Nature Switzerland},
        volume = {LNCS 15011},
        month = {October},
        page = {211 -- 220}
}


@inproceedings{yao_glad_2025,
	address = {Cham},
	title = {{GLAD}: {Towards} {Better} {Reconstruction} with {Global} and {Local} {Adaptive} {Diffusion} {Models} for {Unsupervised} {Anomaly} {Detection}},
	isbn = {978-3-031-73209-6},
	shorttitle = {{GLAD}},
	doi = {10.1007/978-3-031-73209-6_1},
	abstract = {Diffusion models have shown superior performance on unsupervised anomaly detection tasks. Since trained with normal data only, diffusion models tend to reconstruct normal counterparts of test images with certain noises added. However, these methods treat all potential anomalies equally, which may cause two main problems. From the global perspective, the difficulty of reconstructing images with different anomalies is uneven. For example, adding back a missing element is harder than dealing with a scratch, thus requiring a larger number of denoising steps. Therefore, instead of utilizing the same setting for all samples, we propose to predict a particular denoising step for each sample by evaluating the difference between image contents and the priors extracted from diffusion models. From the local perspective, reconstructing abnormal regions differs from normal areas even in the same image. Theoretically, the diffusion model predicts a noise for each step, typically following a standard Gaussian distribution. However, due to the difference between the anomaly and its potential normal counterpart, the predicted noise in abnormal regions will inevitably deviate from the standard Gaussian distribution. To this end, we propose introducing synthetic abnormal samples in training to encourage the diffusion models to break through the limitation of standard Gaussian distribution, and a spatial-adaptive feature fusion scheme is utilized during inference. With the above modifications, we propose a global and local adaptive diffusion model (abbreviated to GLAD) for unsupervised anomaly detection, which introduces appealing flexibility and achieves anomaly-free reconstruction while retaining as much normal information as possible. Extensive experiments are conducted on three commonly used anomaly detection datasets (MVTec-AD, MPDD, and VisA) and a printed circuit board dataset (PCB-Bank) we integrated, showing the effectiveness of the proposed method. The source code and pre-trained models are publicly available at https://github.com/hyao1/GLAD.},
	language = {en},
	booktitle = {Computer {Vision} – {ECCV} 2024},
	publisher = {Springer Nature Switzerland},
	author = {Yao, Hang and Liu, Ming and Yin, Zhicun and Yan, Zifei and Hong, Xiaopeng and Zuo, Wangmeng},
	editor = {Leonardis, Aleš and Ricci, Elisa and Roth, Stefan and Russakovsky, Olga and Sattler, Torsten and Varol, Gül},
	year = {2025},
	keywords = {Adaptive Denoising Process, Diffusion Models, Unsupervised Anomaly Detection},
	pages = {1--17},
	file = {Full Text PDF:/Users/xz/Zotero/storage/8QTD7ACP/Yao et al. - 2025 - GLAD Towards Better Reconstruction with Global an.pdf:application/pdf},
}

@misc{beizaee_mad-ad_2025,
	title = {{MAD}-{AD}: {Masked} {Diffusion} for {Unsupervised} {Brain} {Anomaly} {Detection}},
	shorttitle = {{MAD}-{AD}},
	url = {http://arxiv.org/abs/2502.16943},
	doi = {10.48550/arXiv.2502.16943},
	abstract = {Unsupervised anomaly detection in brain images is crucial for identifying injuries and pathologies without access to labels. However, the accurate localization of anomalies in medical images remains challenging due to the inherent complexity and variability of brain structures and the scarcity of annotated abnormal data. To address this challenge, we propose a novel approach that incorporates masking within diffusion models, leveraging their generative capabilities to learn robust representations of normal brain anatomy. During training, our model processes only normal brain MRI scans and performs a forward diffusion process in the latent space that adds noise to the features of randomly-selected patches. Following a dual objective, the model learns to identify which patches are noisy and recover their original features. This strategy ensures that the model captures intricate patterns of normal brain structures while isolating potential anomalies as noise in the latent space. At inference, the model identifies noisy patches corresponding to anomalies and generates a normal counterpart for these patches by applying a reverse diffusion process. Our method surpasses existing unsupervised anomaly detection techniques, demonstrating superior performance in generating accurate normal counterparts and localizing anomalies. The code is available at hhttps://github.com/farzad-bz/MAD-AD.},
	urldate = {2025-09-19},
	publisher = {arXiv},
	author = {Beizaee, Farzad and Lodygensky, Gregory and Desrosiers, Christian and Dolz, Jose},
	month = jul,
	year = {2025},
	note = {arXiv:2502.16943 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing},
	file = {Preprint PDF:/Users/xz/Zotero/storage/FPGXJUWA/Beizaee et al. - 2025 - MAD-AD Masked Diffusion for Unsupervised Brain An.pdf:application/pdf;Snapshot:/Users/xz/Zotero/storage/4XTLWSPA/2502.html:text/html},
}

@inproceedings{pinaya_fast_2022,
	address = {Cham},
	title = {Fast {Unsupervised} {Brain} {Anomaly} {Detection} and {Segmentation} with {Diffusion} {Models}},
	isbn = {978-3-031-16452-1},
	doi = {10.1007/978-3-031-16452-1_67},
	abstract = {Deep generative models have emerged as promising tools for detecting arbitrary anomalies in data, dispensing with the necessity for manual labelling. Recently, autoregressive transformers have achieved state-of-the-art performance for anomaly detection in medical imaging. Nonetheless, these models still have some intrinsic weaknesses, such as requiring images to be modelled as 1D sequences, the accumulation of errors during the sampling process, and the significant inference times associated with transformers. Denoising diffusion probabilistic models are a class of non-autoregressive generative models recently shown to produce excellent samples in computer vision (surpassing Generative Adversarial Networks), and to achieve log-likelihoods that are competitive with transformers while having relatively fast inference times. Diffusion models can be applied to the latent representations learnt by autoencoders, making them easily scalable and great candidates for application to high dimensional data, such as medical images. Here, we propose a method based on diffusion models to detect and segment anomalies in brain imaging. By training the models on healthy data and then exploring its diffusion and reverse steps across its Markov chain, we can identify anomalous areas in the latent space and hence identify anomalies in the pixel space. Our diffusion models achieve competitive performance compared with autoregressive approaches across a series of experiments with 2D CT and MRI data involving synthetic and real pathological lesions with much reduced inference times, making their usage clinically viable.},
	language = {en},
	booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2022},
	publisher = {Springer Nature Switzerland},
	author = {Pinaya, Walter H. L. and Graham, Mark S. and Gray, Robert and da Costa, Pedro F. and Tudosiu, Petru-Daniel and Wright, Paul and Mah, Yee H. and MacKinnon, Andrew D. and Teo, James T. and Jager, Rolf and Werring, David and Rees, Geraint and Nachev, Parashkev and Ourselin, Sebastien and Cardoso, M. Jorge},
	editor = {Wang, Linwei and Dou, Qi and Fletcher, P. Thomas and Speidel, Stefanie and Li, Shuo},
	year = {2022},
	keywords = {Denoising diffusion probabilistic models, Lesion segmentation, Neuroimaging, Out-of-distribution detection, Unsupervised anomaly detection},
	pages = {705--714},
	file = {Full Text PDF:/Users/xz/Zotero/storage/SSLWPHXY/Pinaya et al. - 2022 - Fast Unsupervised Brain Anomaly Detection and Segm.pdf:application/pdf},
}


@inproceedings{guo_seam-stress_2023,
	title = {{SEAM}-{STRESS}: {A} {Weakly} {Supervised} {Framework} for {Interstitial} {Lung} {Disease} {Segmentation} in {Chest} {CT}},
	shorttitle = {{SEAM}-{STRESS}},
	url = {https://ieeexplore.ieee.org/abstract/document/10230747},
	doi = {10.1109/ISBI53787.2023.10230747},
	abstract = {We propose Self-supervised Equivariant Attention Mechanism with up-Sampling, adapTive thREsholding, and augmented loSSes (SEAM-STRESS) for weakly supervised abnormality segmentation in chest CT images, covering a wide range of abnormal patterns. We introduce a novel point-level loss that allows the utilization of sparse annotations as a weak supervisory signal, outperforming models trained only with image-level labels. Furthermore, we introduce a post-processing adaptive background thresholding strategy to further improve the segmentation masks. Experiments on our internal dataset show the effectiveness of our framework in localizing and segmenting chest CT abnormalities.},
	urldate = {2025-09-19},
	booktitle = {2023 {IEEE} 20th {International} {Symposium} on {Biomedical} {Imaging} ({ISBI})},
	author = {Guo, Xueqi and Abdi, Mohamad and Shinagawa, Yoshihisa and Jerebko, Anna and Farhand, Sepehr},
	month = apr,
	year = {2023},
	note = {ISSN: 1945-8452},
	keywords = {Adaptation models, computed tomography, Computed tomography, Filtering, Image segmentation, interstitial lung disease, Pulmonary diseases, Thresholding (Imaging), Visualization, weakly supervised segmentation},
	pages = {1--4},
	file = {Snapshot:/Users/xz/Zotero/storage/VC2S9Y95/10230747.html:text/html},
}


@misc{krahenbuhl_efficient_2012,
	title = {Efficient {Inference} in {Fully} {Connected} {CRFs} with {Gaussian} {Edge} {Potentials}},
	url = {http://arxiv.org/abs/1210.5644},
	doi = {10.48550/arXiv.1210.5644},
	abstract = {Most state-of-the-art techniques for multi-class image segmentation and labeling use conditional random fields defined over pixels or image regions. While region-level models often feature dense pairwise connectivity, pixel-level models are considerably larger and have only permitted sparse graph structures. In this paper, we consider fully connected CRF models defined on the complete set of pixels in an image. The resulting graphs have billions of edges, making traditional inference algorithms impractical. Our main contribution is a highly efficient approximate inference algorithm for fully connected CRF models in which the pairwise edge potentials are defined by a linear combination of Gaussian kernels. Our experiments demonstrate that dense connectivity at the pixel level substantially improves segmentation and labeling accuracy.},
	urldate = {2025-09-19},
	publisher = {arXiv},
	author = {Krähenbühl, Philipp and Koltun, Vladlen},
	month = oct,
	year = {2012},
	note = {arXiv:1210.5644 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
	annote = {Comment: NIPS 2011},
	file = {Preprint PDF:/Users/xz/Zotero/storage/Z3CRP88M/Krähenbühl and Koltun - 2012 - Efficient Inference in Fully Connected CRFs with G.pdf:application/pdf;Snapshot:/Users/xz/Zotero/storage/N77YNWS8/1210.html:text/html},
}


@article{chaganti_automated_2020-2,
	title = {Automated {Quantification} of {CT} {Patterns} {Associated} with {COVID}-19 from                     {Chest} {CT}},
	volume = {2},
	url = {https://pubs.rsna.org/doi/full/10.1148/ryai.2020200048},
	doi = {10.1148/ryai.2020200048},
	abstract = {Purpose To present a method that automatically segments and quantifies abnormal                             CT patterns commonly present in COVID-19, namely ground-glass opacities                             and consolidations.Materials and Methods In this retrospective study, the proposed method takes as input a                             noncontrast chest CT and segments the lesions, lungs, and lobes in three                             dimensions, based on a dataset of 9749 chest CT volumes. The method                             outputs two combined measures of the severity of lung and lobe                             involvement, quantifying both the extent of COVID-19 abnormalities and                             presence of high opacities, based on deep learning and deep                             reinforcement learning. The first measure of (percentage of opacity,                             percentage of high opacity [PO, PHO]) is global, while the second of                             (lung severity score, lung high opacity score [LSS, LHOS]) is lobe-wise.                             Evaluation of the algorithm is reported on CT studies of 200                             participants (100 COVID-19 confirmed patients and 100 healthy controls)                             from institutions from Canada, Europe, and the United States collected                             between 2002 and April 2020. Ground truth is established by manual                             annotations of lesions, lungs, and lobes. Correlation and regression                             analyses were performed to compare the prediction to the ground                             truth.Results Pearson correlation coefficient between method prediction and ground                             truth for COVID-19 cases was calculated as 0.92 for PO                                 (P {\textless} .001), 0.97 for PHO (P                             {\textless} .001), 0.91 for LSS (P {\textless} .001), and                             0.90 for LHOS (P {\textless} .001). Ninety-eight of 100                             healthy controls had a predicted PO of less than 1\%; two had between 1\%                             and 2\%. Automated processing time to compute the severity scores was 10                             seconds per case compared with 30 minutes required for manual                             annotations.Conclusion A new method segments regions of CT abnormalities associated with                             COVID-19 and computes (PO, PHO), as well as (LSS, LHOS) severity                             scores. Keywords: CT, Lung, Segmentation/Vision/Application Domain,                             Quantification/Vision/Application Domain, Supervised Learning,                             Reinforcement Learning © RSNA, 2021 Supplemental material is available for this                                     article.},
	number = {4},
	urldate = {2025-09-19},
	journal = {Radiology: Artificial Intelligence},
	author = {Chaganti, Shikha and Grenier, Philippe and Balachandran, Abishek and Chabin, Guillaume and Cohen, Stuart and Flohr, Thomas and Georgescu, Bogdan and Grbic, Sasa and Liu, Siqi and Mellot, François and Murray, Nicolas and Nicolaou, Savvas and Parker, William and Re, Thomas and Sanelli, Pina and Sauter, Alexander                             W. and Xu, Zhoubing and Yoo, Youngjin and Ziebandt, Valentin and Comaniciu, Dorin},
	month = jul,
	year = {2020},
	note = {Publisher: Radiological Society of North America},
	pages = {e200048},
	file = {Full Text PDF:/Users/xz/Zotero/storage/MTC69BFH/Chaganti et al. - 2020 - Automated Quantification of CT Patterns Associated.pdf:application/pdf},
}




@inproceedings{loshchilov_decoupled_2018,
	title = {Decoupled {Weight} {Decay} {Regularization}},
	url = {https://openreview.net/forum?id=Bkg6RiCqY7},
	abstract = {L\$\_2\$ regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is {\textbackslash}emph\{not\} the case for adaptive gradient algorithms, such as Adam. While common implementations of these algorithms employ L\$\_2\$ regularization (often calling it ``weight decay'' in what may be misleading due to the inequivalence we expose), we propose a simple modification to recover the original formulation of weight decay regularization by {\textbackslash}emph\{decoupling\} the weight decay from the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam and (ii) substantially improves Adam's generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter). Our proposed decoupled weight decay has already been adopted by many researchers, and the community has implemented it in TensorFlow and PyTorch; the complete source code for our experiments is available at {\textbackslash}url\{https://github.com/loshchil/AdamW-and-SGDW\}},
	language = {en},
	urldate = {2025-09-19},
	author = {Loshchilov, Ilya and Hutter, Frank},
	month = sep,
	year = {2018},
	file = {Full Text PDF:/Users/xz/Zotero/storage/9PVWJC4P/Loshchilov and Hutter - 2018 - Decoupled Weight Decay Regularization.pdf:application/pdf},
}



@misc{rombach_high-resolution_2022,
	title = {High-{Resolution} {Image} {Synthesis} with {Latent} {Diffusion} {Models}},
	url = {http://arxiv.org/abs/2112.10752},
	doi = {10.48550/arXiv.2112.10752},
	abstract = {By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. However, since these models typically operate directly in pixel space, optimization of powerful DMs often consumes hundreds of GPU days and inference is expensive due to sequential evaluations. To enable DM training on limited computational resources while retaining their quality and flexibility, we apply them in the latent space of powerful pretrained autoencoders. In contrast to previous work, training diffusion models on such a representation allows for the first time to reach a near-optimal point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the model architecture, we turn diffusion models into powerful and flexible generators for general conditioning inputs such as text or bounding boxes and high-resolution synthesis becomes possible in a convolutional manner. Our latent diffusion models (LDMs) achieve a new state of the art for image inpainting and highly competitive performance on various tasks, including unconditional image generation, semantic scene synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel-based DMs. Code is available at https://github.com/CompVis/latent-diffusion .},
	urldate = {2025-09-19},
	publisher = {arXiv},
	author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Björn},
	month = apr,
	year = {2022},
	note = {arXiv:2112.10752 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	annote = {Comment: CVPR 2022},
	file = {Preprint PDF:/Users/xz/Zotero/storage/HERNQ79Y/Rombach et al. - 2022 - High-Resolution Image Synthesis with Latent Diffus.pdf:application/pdf;Snapshot:/Users/xz/Zotero/storage/GPEFPTBP/2112.html:text/html},
}

@inproceedings{perez_film_2018,
	address = {New Orleans, Louisiana, USA},
	series = {{AAAI}'18/{IAAI}'18/{EAAI}'18},
	title = {{FiLM}: visual reasoning with a general conditioning layer},
	isbn = {978-1-57735-800-8},
	shorttitle = {{FiLM}},
	abstract = {We introduce a general-purpose conditioning method for neural networks called FiLM: Feature-wise Linear Modulation. FiLM layers influence neural network computation via a simple, feature-wise affine transformation based on conditioning information. We show that FiLM layers are highly effective for visual reasoning — answering image-related questions which require a multi-step, high-level process — a task which has proven difficult for standard deep learning methods that do not explicitly model reasoning. Specifically, we show on visual reasoning tasks that FiLM layers 1) halve state-of-the-art error for the CLEVR benchmark, 2) modulate features in a coherent manner, 3) are robust to ablations and architectural modifications, and 4) generalize well to challenging, new data from few examples or even zero-shot.},
	urldate = {2025-09-19},
	booktitle = {Proceedings of the {Thirty}-{Second} {AAAI} {Conference} on {Artificial} {Intelligence} and {Thirtieth} {Innovative} {Applications} of {Artificial} {Intelligence} {Conference} and {Eighth} {AAAI} {Symposium} on {Educational} {Advances} in {Artificial} {Intelligence}},
	publisher = {AAAI Press},
	author = {Perez, Ethan and Strub, Florian and de Vries, Harm and Dumoulin, Vincent and Courville, Aaron},
	month = feb,
	year = {2018},
	pages = {3942--3951},
	file = {Full Text PDF:/Users/xz/Zotero/storage/29L6EI38/Perez et al. - 2018 - FiLM visual reasoning with a general conditioning.pdf:application/pdf},
}



@article{abdelhalim_data_2021-1,
	title = {Data augmentation for skin lesion using self-attention based progressive generative adversarial network},
	volume = {165},
	issn = {0957-4174},
	url = {https://www.sciencedirect.com/science/article/pii/S0957417420307132},
	doi = {10.1016/j.eswa.2020.113922},
	abstract = {While recent years have witnessed the remarkable success of deep learning methods in automated skin lesion detection systems, there still exists a gap between manual assessment of experts and automated evaluation of computers. The reason behind such a gap is the deep learning models demand considerable amounts of data, while the availability of annotated images is often limited. Data Augmentation (DA) is one way to mitigate the lack of labeled data; however, the augmented images intrinsically have a similar distribution to the original ones, leading to limited performance improvement. To satisfy the data lack in the real image distribution, we synthesize skin lesion images – realistic but completely different from the original ones – using Generative Adversarial Networks (GANs). In this paper, we propose the Self-attention Progressive Growing of GANs (SPGGANs) to generate fine-grained 256 × 256 skin lesion images for Convolutional Neural Network-based melanoma detection, which is challenging via conventional GANs; difficulties arise due to unstable GAN training with high resolution and a variety of skin lesions in size, shape, and location. In SPGGAN, details can be generated using aggregated information from all feature locations. Moreover, the discriminator can monitor that highly detailed features in distant portions of the image are consistent with each other. Furthermore, the Two-Timescale Update Rule (TTUR) is applied to SPGGAN (SPGGAN-TTUR) to improve stability while generating 256 × 256 skin lesion images. SPGGAN-TTUR is evaluated on data generation and classification tasks using the HAM10000 dataset. Our results confirm the importance of our proposed GAN-based DA approach for training skin lesion classifiers and indicate that it can lead to statistically significant improvements (p-value {\textless}0.05) in the sensitivity (recall) over non-augmented and augmented, with classical DA, counterparts. In general, in the case of all classes, The sensitivity improvements were 5.6\% and 2.5\% over non-augmented and augmented (with the best DA scheme) counterparts, respectively. Specifically, in the case of melanoma class, the sensitivity improvements were 13.8\% and 8.6\%. We believe that the proposed approach can be adopted in clinical practice to improve the sensitivity of automated skin lesion detection in dermoscopic images and thus support dermatologists’ efforts to improve melanoma diagnosis.},
	urldate = {2025-09-20},
	journal = {Expert Systems with Applications},
	author = {Abdelhalim, Ibrahim Saad Aly and Mohamed, Mamdouh Farouk and Mahdy, Yousef Bassyouni},
	month = mar,
	year = {2021},
	keywords = {Data augmentation, Data imbalance, Deep learning, Generative models, Skin cancer},
	pages = {113922},
	file = {ScienceDirect Snapshot:/Users/xz/Zotero/storage/CWRCKIHD/S0957417420307132.html:text/html;Submitted Version:/Users/xz/Zotero/storage/5H7B9FEF/Abdelhalim et al. - 2021 - Data augmentation for skin lesion using self-atten.pdf:application/pdf},
}


@article{li_tumorgan_2020,
	title = {{TumorGAN}: {A} {Multi}-{Modal} {Data} {Augmentation} {Framework} for {Brain} {Tumor} {Segmentation}},
	volume = {20},
	issn = {1424-8220},
	shorttitle = {{TumorGAN}},
	doi = {10.3390/s20154203},
	abstract = {The high human labor demand involved in collecting paired medical imaging data severely impedes the application of deep learning methods to medical image processing tasks such as tumor segmentation. The situation is further worsened when collecting multi-modal image pairs. However, this issue can be resolved through the help of generative adversarial networks, which can be used to generate realistic images. In this work, we propose a novel framework, named TumorGAN, to generate image segmentation pairs based on unpaired adversarial training. To improve the quality of the generated images, we introduce a regional perceptual loss to enhance the performance of the discriminator. We also develop a regional L1 loss to constrain the color of the imaged brain tissue. Finally, we verify the performance of TumorGAN on a public brain tumor data set, BraTS 2017. The experimental results demonstrate that the synthetic data pairs generated by our proposed method can practically improve tumor segmentation performance when applied to segmentation network training.},
	language = {eng},
	number = {15},
	journal = {Sensors (Basel, Switzerland)},
	author = {Li, Qingyun and Yu, Zhibin and Wang, Yubo and Zheng, Haiyong},
	month = jul,
	year = {2020},
	pmid = {32731598},
	pmcid = {PMC7435374},
	keywords = {Brain, Brain Neoplasms, brain tumor segmentation, generative adversarial network, Humans, Image Processing, Computer-Assisted, image-to-image, medical image augmentation},
	pages = {4203},
	file = {Full Text:/Users/xz/Zotero/storage/3A2LX6QY/Li et al. - 2020 - TumorGAN A Multi-Modal Data Augmentation Framewor.pdf:application/pdf},
}


@inproceedings{huang_augpaste_2022,
	address = {Berlin, Heidelberg},
	title = {{AugPaste}: {One}-{Shot} {Anomaly} {Detection} for {Medical} {Images}},
	isbn = {978-3-031-16524-5},
	shorttitle = {{AugPaste}},
	url = {https://doi.org/10.1007/978-3-031-16525-2_1},
	doi = {10.1007/978-3-031-16525-2_1},
	abstract = {Due to the high cost of manually annotating medical images, especially for large-scale datasets, anomaly detection has been explored through training models with only normal data. Lacking prior knowledge of true anomalies is the main reason for the limited application of previous anomaly detection methods, especially in the medical image analysis realm. In this work, we propose a one-shot anomaly detection framework, namely AugPaste, that utilizes true anomalies from a single annotated sample and synthesizes artificial anomalous samples for anomaly detection. First, a lesion bank is constructed by applying augmentation to randomly selected lesion patches. Then, MixUp is adopted to paste patches from the lesion bank at random positions in normal images to synthesize anomalous samples for training. Finally, a classification network is trained using the synthetic abnormal samples and the true normal data. Extensive experiments are conducted on two publicly-available medical image datasets with different types of abnormalities. On both datasets, our proposed AugPaste largely outperforms several state-of-the-art unsupervised and semi-supervised anomaly detection methods, and is on a par with the fully-supervised counterpart. To note, AugPaste is even better than the fully-supervised method in detecting early-stage diabetic retinopathy.},
	urldate = {2025-09-19},
	booktitle = {Ophthalmic {Medical} {Image} {Analysis}: 9th {International} {Workshop}, {OMIA} 2022, {Held} in {Conjunction} with {MICCAI} 2022, {Singapore}, {Singapore}, {September} 22, 2022, {Proceedings}},
	publisher = {Springer-Verlag},
	author = {Huang, Weikai and Huang, Yijin and Tang, Xiaoying},
	month = sep,
	year = {2022},
	pages = {1--11},
}


@article{bercea_evaluating_2025,
	title = {Evaluating normative representation learning in generative {AI} for robust anomaly detection in brain imaging},
	volume = {16},
	issn = {2041-1723},
	doi = {10.1038/s41467-025-56321-y},
	abstract = {Normative representation learning focuses on understanding the typical anatomical distributions from large datasets of medical scans from healthy individuals. Generative Artificial Intelligence (AI) leverages this attribute to synthesize images that accurately reflect these normative patterns. This capability enables the AI allowing them to effectively detect and correct anomalies in new, unseen pathological data without the need for expert labeling. Traditional anomaly detection methods often evaluate the anomaly detection performance, overlooking the crucial role of normative learning. In our analysis, we introduce novel metrics, specifically designed to evaluate this facet in AI models. We apply these metrics across various generative AI frameworks, including advanced diffusion models, and rigorously test them against complex and diverse brain pathologies. In addition, we conduct a large multi-reader study to compare these metrics to experts' evaluations. Our analysis demonstrates that models proficient in normative learning exhibit exceptional versatility, adeptly detecting a wide range of unseen medical conditions. Our code is available at https://github.com/compai-lab/2024-ncomms-bercea.git .},
	language = {eng},
	number = {1},
	journal = {Nature Communications},
	author = {Bercea, Cosmin I. and Wiestler, Benedikt and Rueckert, Daniel and Schnabel, Julia A.},
	month = feb,
	year = {2025},
	pmid = {39948337},
	pmcid = {PMC11825664},
	keywords = {Algorithms, Artificial Intelligence, Brain, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Neuroimaging},
	pages = {1624},
}

@article{cai_medianomaly_2025,
	title = {{MedIAnomaly}: {A} comparative study of anomaly detection in medical images},
	volume = {102},
	issn = {1361-8415},
	shorttitle = {{MedIAnomaly}},
	url = {https://www.sciencedirect.com/science/article/pii/S1361841525000489},
	doi = {10.1016/j.media.2025.103500},
	abstract = {Anomaly detection (AD) aims at detecting abnormal samples that deviate from the expected normal patterns. Generally, it can be trained merely on normal data, without a requirement for abnormal samples, and thereby plays an important role in rare disease recognition and health screening in the medical domain. Despite the emergence of numerous methods for medical AD, the lack of a fair and comprehensive evaluation causes ambiguous conclusions and hinders the development of this field. To address this problem, this paper builds a benchmark with unified comparison. Seven medical datasets with five image modalities, including chest X-rays, brain MRIs, retinal fundus images, dermatoscopic images, and histopathology images, are curated for extensive evaluation. Thirty typical AD methods, including reconstruction and self-supervised learning-based methods, are involved in comparison of image-level anomaly classification and pixel-level anomaly segmentation. Furthermore, for the first time, we systematically investigate the effect of key components in existing methods, revealing unresolved challenges and potential future directions. The datasets and code are available at https://github.com/caiyu6666/MedIAnomaly.},
	urldate = {2025-09-20},
	journal = {Medical Image Analysis},
	author = {Cai, Yu and Zhang, Weiwen and Chen, Hao and Cheng, Kwang-Ting},
	month = may,
	year = {2025},
	keywords = {Anomaly detection, Benchmark, Survey},
	pages = {103500},
	file = {ScienceDirect Snapshot:/Users/xz/Zotero/storage/M9QWXRXC/S1361841525000489.html:text/html},
}


@inproceedings{song_score-based_2020,
	title = {Score-{Based} {Generative} {Modeling} through {Stochastic} {Differential} {Equations}},
	url = {https://openreview.net/forum?id=PxTIG12RRHS},
	abstract = {Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially, the reverse-time SDE depends only on the time-dependent gradient field (a.k.a., score) of the perturbed data distribution. By leveraging advances in score-based generative modeling, we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples. We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling, allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE. We also derive an equivalent neural ODE that samples from the same distribution as the SDE, but additionally enables exact likelihood computation, and improved sampling efficiency. In addition, we provide a new way to solve inverse problems with score-based models, as demonstrated with experiments on class-conditional generation, image inpainting, and colorization. Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9.89 and FID of 2.20, a competitive likelihood of 2.99 bits/dim, and demonstrate high fidelity generation of \$1024{\textbackslash}times 1024\$ images for the first time from a score-based generative model.},
	language = {en},
	urldate = {2025-09-20},
	author = {Song, Yang and Sohl-Dickstein, Jascha and Kingma, Diederik P. and Kumar, Abhishek and Ermon, Stefano and Poole, Ben},
	month = oct,
	year = {2020},
	file = {Full Text PDF:/Users/xz/Zotero/storage/TLXTXY5M/Song et al. - 2020 - Score-Based Generative Modeling through Stochastic.pdf:application/pdf},
}


@inproceedings{yu_adversarial_2023,
	title = {Adversarial {Denoising} {Diffusion} {Model} for {Unsupervised} {Anomaly} {Detection}},
	url = {https://openreview.net/forum?id=PvHhhn1iX9},
	abstract = {In this paper, we propose the Adversarial Denoising Diffusion Model (ADDM). The ADDM is based on the Denoising Diffusion Probabilistic Model (DDPM) but complementarily trained by adversarial learning. The proposed adversarial learning is achieved by classifying model-based denoised samples and samples to which random Gaussian noise is added to a specific sampling step. With the addition of explicit adversarial learning on data samples, ADDM can learn the semantic characteristics of the data more robustly during training, which achieves a similar data sampling performance with much fewer sampling steps than DDPM. We apply ADDM to anomaly detection in unsupervised MRI images. Experimental results show that the proposed ADDM outperformed existing generative model-based unsupervised anomaly detection methods. In particular, compared to other DDPM-based anomaly detection methods, the proposed ADDM shows better performance with the same number of sampling steps and similar performance with 50\% fewer sampling steps.},
	language = {en},
	urldate = {2025-09-20},
	author = {Yu, Jongmin and Oh, Hyeontaek and Yang, Jinhong},
	month = oct,
	year = {2023},
	file = {Full Text PDF:/Users/xz/Zotero/storage/YEXR4ANU/Yu et al. - 2023 - Adversarial Denoising Diffusion Model for Unsuperv.pdf:application/pdf},
}


@misc{ma_medsam2_2025,
	title = {{MedSAM2}: {Segment} {Anything} in {3D} {Medical} {Images} and {Videos}},
	shorttitle = {{MedSAM2}},
	url = {http://arxiv.org/abs/2504.03600},
	doi = {10.48550/arXiv.2504.03600},
	abstract = {Medical image and video segmentation is a critical task for precision medicine, which has witnessed considerable progress in developing task or modality-specific and generalist models for 2D images. However, there have been limited studies on building general-purpose models for 3D images and videos with comprehensive user studies. Here, we present MedSAM2, a promptable segmentation foundation model for 3D image and video segmentation. The model is developed by fine-tuning the Segment Anything Model 2 on a large medical dataset with over 455,000 3D image-mask pairs and 76,000 frames, outperforming previous models across a wide range of organs, lesions, and imaging modalities. Furthermore, we implement a human-in-the-loop pipeline to facilitate the creation of large-scale datasets resulting in, to the best of our knowledge, the most extensive user study to date, involving the annotation of 5,000 CT lesions, 3,984 liver MRI lesions, and 251,550 echocardiogram video frames, demonstrating that MedSAM2 can reduce manual costs by more than 85\%. MedSAM2 is also integrated into widely used platforms with user-friendly interfaces for local and cloud deployment, making it a practical tool for supporting efficient, scalable, and high-quality segmentation in both research and healthcare environments.},
	urldate = {2025-12-10},
	publisher = {arXiv},
	author = {Ma, Jun and Yang, Zongxin and Kim, Sumin and Chen, Bihui and Baharoon, Mohammed and Fallahpour, Adibvafa and Asakereh, Reza and Lyu, Hongwei and Wang, Bo},
	month = apr,
	year = {2025},
	note = {arXiv:2504.03600 [eess]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing},
	annote = {Comment: https://medsam2.github.io/},
	file = {Preprint PDF:/Users/xz/Zotero/storage/E63BBWUJ/Ma et al. - 2025 - MedSAM2 Segment Anything in 3D Medical Images and.pdf:application/pdf;Snapshot:/Users/xz/Zotero/storage/YR45W7QQ/2504.html:text/html},
}

@article{biondi_classification_2021,
	title = {Classification {Performance} for {COVID} {Patient} {Prognosis} from {Automatic} {AI} {Segmentation}—{A} {Single}-{Center} {Study}},
	volume = {11},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2076-3417},
	url = {https://www.mdpi.com/2076-3417/11/12/5438},
	doi = {10.3390/app11125438},
	abstract = {Background: COVID assessment can be performed using the recently developed individual risk score (prediction of severe respiratory failure in hospitalized patients with SARS-COV2 infection, PREDI-CO score) based on High Resolution Computed Tomography. In this study, we evaluated the possibility of automatizing this estimation using semi-supervised AI-based Radiomics, leveraging the possibility of performing non-supervised segmentation of ground-glass areas. Methods: We collected 92 from patients treated in the IRCCS Sant’Orsola-Malpighi Policlinic and public databases; each lung was segmented using a pre-trained AI method; ground-glass opacity was identified using a novel, non-supervised approach; radiomic measurements were collected and used to predict clinically relevant scores, with particular focus on mortality and the PREDI-CO score. We compared the prediction obtained through different machine learning approaches. Results: All the methods obtained a well-balanced accuracy (70\%) on the PREDI-CO score but did not obtain satisfying results on other clinical characteristics due to unbalance between the classes. Conclusions: Semi-supervised segmentation, implemented using a combination of non-supervised segmentation and feature extraction, seems to be a viable approach for patient stratification and could be leveraged to train more complex models. This would be useful in a high-demand situation similar to the current pandemic to support gold-standard segmentation for AI training.},
	language = {en},
	number = {12},
	urldate = {2025-12-10},
	journal = {Applied Sciences},
	author = {Biondi, Riccardo and Curti, Nico and Coppola, Francesca and Giampieri, Enrico and Vara, Giulio and Bartoletti, Michele and Cattabriga, Arrigo and Cocozza, Maria Adriana and Ciccarese, Federica and De Benedittis, Caterina and Cercenelli, Laura and Bortolani, Barbara and Marcelli, Emanuela and Pierotti, Luisa and Strigari, Lidia and Viale, Pierluigi and Golfieri, Rita and Castellani, Gastone},
	month = jan,
	year = {2021},
	note = {Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {artificial intelligence, machine and deep learning, medical imaging, radiomics},
	pages = {5438},
	file = {Full Text PDF:/Users/xz/Zotero/storage/L6JN8UL4/Biondi et al. - 2021 - Classification Performance for COVID Patient Progn.pdf:application/pdf},
}


@inproceedings{baur_steganomaly_2020,
	address = {Cham},
	title = {{SteGANomaly}: {Inhibiting} {CycleGAN} {Steganography} for {Unsupervised} {Anomaly} {Detection} in {Brain} {MRI}},
	isbn = {978-3-030-59713-9},
	shorttitle = {{SteGANomaly}},
	doi = {10.1007/978-3-030-59713-9_69},
	abstract = {Recently, it has been shown that CycleGANs are masters of steganography. They cannot only learn reliable mappings between two distributions without calling for paired training data, but can effectively hide information unseen during training in mapping results from which input data can be recovered almost perfectly. When preventing this during training, CycleGANs actually map samples much closer to the training distribution. Here, we propose to leverage this effect in the context of trending unsupervised anomaly detection, which primarily relies on modeling healthy anatomy with generative models. Here, we embed anomaly detection into a CycleGAN-based style-transfer framework, which is trained to translate healthy brain MR images to a simulated distribution with lower entropy and vice versa. By filtering high frequency, low amplitude signals from lower entropy samples during training, the resulting model suppresses anomalies in reconstructions of the input data at test time. Similar to Autoencoder and GAN-based anomaly detection methods, this allows us to delineate pathologies directly from residuals between input and reconstruction. Various ablative studies and comparisons to state-of-the-art methods highlight the potential of our method.},
	language = {en},
	booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2020},
	publisher = {Springer International Publishing},
	author = {Baur, Christoph and Graf, Robert and Wiestler, Benedikt and Albarqouni, Shadi and Navab, Nassir},
	editor = {Martel, Anne L. and Abolmaesumi, Purang and Stoyanov, Danail and Mateus, Diana and Zuluaga, Maria A. and Zhou, S. Kevin and Racoceanu, Daniel and Joskowicz, Leo},
	year = {2020},
	pages = {718--727},
	file = {Full Text PDF:/Users/xz/Zotero/storage/HDJVRDXG/Baur et al. - 2020 - SteGANomaly Inhibiting CycleGAN Steganography for.pdf:application/pdf},
}

@inproceedings{chen_unsupervised_2019,
	address = {Cham},
	title = {Unsupervised {Lesion} {Detection} with {Locally} {Gaussian} {Approximation}},
	isbn = {978-3-030-32692-0},
	doi = {10.1007/978-3-030-32692-0_41},
	abstract = {Generative models have recently been applied to unsupervised lesion detection, where a distribution of normal data, i.e. the normative distribution, is learned and lesions are detected as out-of-distribu-tion regions. However, directly calculating the probability for the lesion region using the normative distribution is intractable. In this work, we address this issue by approximating the normative distribution with local Gaussian approximation and evaluating the probability of test samples in an iterative manner. We show that the local Gaussian approximator can be applied to several auto-encoding models to perform image restoration and unsupervised lesion detection. The proposed method is evaluated on the BraTS Challenge dataset, where the proposed method shows improved detection and achieves state-of-the-art results.},
	language = {en},
	booktitle = {Machine {Learning} in {Medical} {Imaging}},
	publisher = {Springer International Publishing},
	author = {Chen, Xiaoran and Pawlowski, Nick and Glocker, Ben and Konukoglu, Ender},
	editor = {Suk, Heung-Il and Liu, Mingxia and Yan, Pingkun and Lian, Chunfeng},
	year = {2019},
	pages = {355--363},
	file = {Full Text PDF:/Users/xz/Zotero/storage/ASEXEQCD/Chen et al. - 2019 - Unsupervised Lesion Detection with Locally Gaussia.pdf:application/pdf},
}


@article{uzunova_unsupervised_2019,
	title = {Unsupervised pathology detection in medical images using conditional variational autoencoders},
	volume = {14},
	issn = {1861-6429},
	url = {https://doi.org/10.1007/s11548-018-1898-0},
	doi = {10.1007/s11548-018-1898-0},
	abstract = {Pathology detection in medical image data is an important but a rather complicated task. In particular, the big variability of the pathologies is a challenge to automatic detection methods and even to machine learning methods. Supervised algorithms would usually learn the appearance of a single pathological structure based on a large annotated dataset. As such data is not usually available, especially in large amounts, in this work we pursue a different unsupervised approach.},
	language = {en},
	number = {3},
	urldate = {2026-01-25},
	journal = {International Journal of Computer Assisted Radiology and Surgery},
	author = {Uzunova, Hristina and Schultz, Sandra and Handels, Heinz and Ehrhardt, Jan},
	month = mar,
	year = {2019},
	keywords = {Conditional variational autoencoder, Image registration, Unsupervised pathology detection},
	pages = {451--461},
}


@misc{lu_anomaly_2018-1,
	title = {Anomaly {Detection} for {Skin} {Disease} {Images} {Using} {Variational} {Autoencoder}},
	url = {http://arxiv.org/abs/1807.01349},
	doi = {10.48550/arXiv.1807.01349},
	abstract = {In this paper, we demonstrate the potential of applying Variational Autoencoder (VAE) [10] for anomaly detection in skin disease images. VAE is a class of deep generative models which is trained by maximizing the evidence lower bound of data distribution [10]. When trained on only normal data, the resulting model is able to perform efficient inference and to determine if a test image is normal or not. We perform experiments on ISIC2018 Challenge Disease Classification dataset (Task 3) and compare different methods to use VAE to detect anomaly. The model is able to detect all diseases with 0.779 AUCROC. If we focus on specific diseases, the model is able to detect melanoma with 0.864 AUCROC and detect actinic keratosis with 0.872 AUCROC, even if it only sees the images of nevus. To the best of our knowledge, this is the first applied work of deep generative models for anomaly detection in dermatology.},
	urldate = {2026-01-25},
	publisher = {arXiv},
	author = {Lu, Yuchen and Xu, Peng},
	month = jul,
	year = {2018},
	note = {arXiv:1807.01349 [cs]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	annote = {Comment: 8 pages, 2 figures, submitted to ISIC Skin Image Analysis Workshop and Challenge at MICCAI 2018},
	file = {Preprint PDF:/Users/xz/Zotero/storage/QHUDNVZU/Lu and Xu - 2018 - Anomaly Detection for Skin Disease Images Using Va.pdf:application/pdf;Snapshot:/Users/xz/Zotero/storage/83TFLXQ9/1807.html:text/html},
}

@article{han_madgan_2021-1,
	title = {{MADGAN}: unsupervised medical anomaly detection {GAN} using multiple adjacent brain {MRI} slice reconstruction},
	volume = {22},
	issn = {1471-2105},
	shorttitle = {{MADGAN}},
	url = {https://doi.org/10.1186/s12859-020-03936-1},
	doi = {10.1186/s12859-020-03936-1},
	abstract = {Unsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a 2D/3D single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer’s disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with either disease stages, various (i.e., more than two types of) diseases, or multi-sequence magnetic resonance imaging (MRI) scans.},
	language = {en},
	number = {2},
	urldate = {2026-01-25},
	journal = {BMC Bioinformatics},
	author = {Han, Changhee and Rundo, Leonardo and Murao, Kohei and Noguchi, Tomoyuki and Shimahara, Yuki and Milacski, Zoltán Ádám and Koshino, Saori and Sala, Evis and Nakayama, Hideki and Satoh, Shin’ichi},
	month = apr,
	year = {2021},
	keywords = {Brain MRI reconstruction, Generative adversarial networks, Self-attention, Unsupervised anomaly detection, Various disease diagnosis},
	pages = {31},
	file = {Full Text PDF:/Users/xz/Zotero/storage/PGK337FB/Han et al. - 2021 - MADGAN unsupervised medical anomaly detection GAN.pdf:application/pdf},
}



@article{ghesu_multi-scale_2019,
	title = {Multi-{Scale} {Deep} {Reinforcement} {Learning} for {Real}-{Time} {3D}-{Landmark} {Detection} in {CT} {Scans}},
	volume = {41},
	issn = {1939-3539},
	url = {https://ieeexplore.ieee.org/document/8187667},
	doi = {10.1109/TPAMI.2017.2782687},
	abstract = {Robust and fast detection of anatomical structures is a prerequisite for both diagnostic and interventional medical image analysis. Current solutions for anatomy detection are typically based on machine learning techniques that exploit large annotated image databases in order to learn the appearance of the captured anatomy. These solutions are subject to several limitations, including the use of suboptimal feature engineering techniques and most importantly the use of computationally suboptimal search-schemes for anatomy detection. To address these issues, we propose a method that follows a new paradigm by reformulating the detection problem as a behavior learning task for an artificial agent. We couple the modeling of the anatomy appearance and the object search in a unified behavioral framework, using the capabilities of deep reinforcement learning and multi-scale image analysis. In other words, an artificial agent is trained not only to distinguish the target anatomical object from the rest of the body but also how to find the object by learning and following an optimal navigation path to the target object in the imaged volumetric space. We evaluated our approach on 1487 3D-CT volumes from 532 patients, totaling over 500,000 image slices and show that it significantly outperforms state-of-the-art solutions on detecting several anatomical structures with no failed cases from a clinical acceptance perspective, while also achieving a 20-30 percent higher detection accuracy. Most importantly, we improve the detection-speed of the reference methods by 2-3 orders of magnitude, achieving unmatched real-time performance on large 3D-CT scans.},
	number = {1},
	urldate = {2025-12-10},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	author = {Ghesu, Florin-Cristian and Georgescu, Bogdan and Zheng, Yefeng and Grbic, Sasa and Maier, Andreas and Hornegger, Joachim and Comaniciu, Dorin},
	month = jan,
	year = {2019},
	keywords = {Biomedical imaging, Deep learning, deep reinforcement learning, intelligent localization, Machine learning, medical image analysis, multi-scale, real-time detection, Real-time systems, scale-space modeling, Search problems, three-dimensional (3D) object detection, Three-dimensional displays, Training},
	pages = {176--189},
	file = {Full Text PDF:/Users/xz/Zotero/storage/V35V5IVL/Ghesu et al. - 2019 - Multi-Scale Deep Reinforcement Learning for Real-T.pdf:application/pdf},
}

@inproceedings{yang_automatic_2017,
	address = {Cham},
	title = {Automatic {Liver} {Segmentation} {Using} an {Adversarial} {Image}-to-{Image} {Network}},
	isbn = {978-3-319-66179-7},
	doi = {10.1007/978-3-319-66179-7_58},
	abstract = {Automatic liver segmentation in 3D medical images is essential in many clinical applications, such as pathological diagnosis of hepatic diseases, surgical planning, and postoperative assessment. However, it is still a very challenging task due to the complex background, fuzzy boundary, and various appearance of liver. In this paper, we propose an automatic and efficient algorithm to segment liver from 3D CT volumes. A deep image-to-image network (DI2IN) is first deployed to generate the liver segmentation, employing a convolutional encoder-decoder architecture combined with multi-level feature concatenation and deep supervision. Then an adversarial network is utilized during training process to discriminate the output of DI2IN from ground truth, which further boosts the performance of DI2IN. The proposed method is trained on an annotated dataset of 1000 CT volumes with various different scanning protocols (e.g., contrast and non-contrast, various resolution and position) and large variations in populations (e.g., ages and pathology). Our approach outperforms the state-of-the-art solutions in terms of segmentation accuracy and computing efficiency.},
	language = {en},
	booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} -- {MICCAI} 2017},
	publisher = {Springer International Publishing},
	author = {Yang, Dong and Xu, Daguang and Zhou, S. Kevin and Georgescu, Bogdan and Chen, Mingqing and Grbic, Sasa and Metaxas, Dimitris and Comaniciu, Dorin},
	editor = {Descoteaux, Maxime and Maier-Hein, Lena and Franz, Alfred and Jannin, Pierre and Collins, D. Louis and Duchesne, Simon},
	year = {2017},
	pages = {507--515},
	file = {Full Text PDF:/Users/xz/Zotero/storage/UW2K29XQ/Yang et al. - 2017 - Automatic Liver Segmentation Using an Adversarial .pdf:application/pdf},
}


@inproceedings{park_semantic_2019,
	title = {Semantic {Image} {Synthesis} {With} {Spatially}-{Adaptive} {Normalization}},
	url = {https://ieeexplore.ieee.org/document/8953676},
	doi = {10.1109/CVPR.2019.00244},
	abstract = {We propose spatially-adaptive normalization, a simple but effective layer for synthesizing photorealistic images given an input semantic layout. Previous methods directly feed the semantic layout as input to the network, forcing the network to memorize the information throughout all the layers. Instead, we propose using the input layout for modulating the activations in normalization layers through a spatially-adaptive, learned affine transformation. Experiments on several challenging datasets demonstrate the superiority of our method compared to existing approaches, regarding both visual fidelity and alignment with input layouts. Finally, our model allows users to easily control the style and content of image synthesis results as well as create multi-modal results. Code is available upon publication.},
	urldate = {2026-01-24},
	booktitle = {2019 {IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})},
	author = {Park, Taesung and Liu, Ming-Yu and Wang, Ting-Chun and Zhu, Jun-Yan},
	month = jun,
	year = {2019},
	note = {ISSN: 2575-7075},
	keywords = {Computational modeling, Computational Photography, Computer vision, Convolution, Deep Learning, Feeds, Image and Video Synthesis, Image synthesis, Layout, Pattern recognition, Photorealistic images, Semantics, Vision + Graphics, Visualization},
	pages = {2332--2341},
	file = {Snapshot:/Users/xz/Zotero/storage/AZWIG2Z5/8953676.html:text/html;Submitted Version:/Users/xz/Zotero/storage/WVPBFV8C/Park et al. - 2019 - Semantic Image Synthesis With Spatially-Adaptive N.pdf:application/pdf},
}


@article{pai_foundation_2024,
	title = {Foundation model for cancer imaging biomarkers},
	volume = {6},
	copyright = {2024 The Author(s)},
	issn = {2522-5839},
	url = {https://www.nature.com/articles/s42256-024-00807-9},
	doi = {10.1038/s42256-024-00807-9},
	abstract = {Foundation models in deep learning are characterized by a single large-scale model trained on vast amounts of data serving as the foundation for various downstream tasks. Foundation models are generally trained using self-supervised learning and excel in reducing the demand for training samples in downstream applications. This is especially important in medicine, where large labelled datasets are often scarce. Here, we developed a foundation model for cancer imaging biomarker discovery by training a convolutional encoder through self-supervised learning using a comprehensive dataset of 11,467 radiographic lesions. The foundation model was evaluated in distinct and clinically relevant applications of cancer imaging-based biomarkers. We found that it facilitated better and more efficient learning of imaging biomarkers and yielded task-specific models that significantly outperformed conventional supervised and other state-of-the-art pretrained implementations on downstream tasks, especially when training dataset sizes were very limited. Furthermore, the foundation model was more stable to input variations and showed strong associations with underlying biology. Our results demonstrate the tremendous potential of foundation models in discovering new imaging biomarkers that may extend to other clinical use cases and can accelerate the widespread translation of imaging biomarkers into clinical settings.},
	language = {en},
	number = {3},
	urldate = {2026-01-25},
	journal = {Nature Machine Intelligence},
	author = {Pai, Suraj and Bontempi, Dennis and Hadzic, Ibrahim and Prudente, Vasco and Sokač, Mateo and Chaunzwa, Tafadzwa L. and Bernatz, Simon and Hosny, Ahmed and Mak, Raymond H. and Birkbak, Nicolai J. and Aerts, Hugo J. W. L.},
	month = mar,
	year = {2024},
	note = {Publisher: Nature Publishing Group},
	keywords = {Biomarkers, Cancer imaging, Tumour biomarkers},
	pages = {354--367},
	file = {Full Text PDF:/Users/xz/Zotero/storage/YTNXL657/Pai et al. - 2024 - Foundation model for cancer imaging biomarkers.pdf:application/pdf},
}


@inproceedings{guo_unsupervised_2025,
	title = {Unsupervised {Abnormality} {Segmentation} in {Chest} {CT} with {Anatomy}-{Guided} {Latent} {Diffusion} {Model} and {Adaptive} {Thresholding}},
	url = {https://openaccess.thecvf.com/content/ICCV2025W/CVAMD/html/Guo_Unsupervised_Abnormality_Segmentation_in_Chest_CT_with_Anatomy-Guided_Latent_Diffusion_ICCVW_2025_paper.html},
	language = {en},
	urldate = {2026-03-10},
	author = {Guo, Xueqi and Shinagawa, Yoshihisa and Farhand, Sepehr and Yerebakan, Halid and Iyer, Kritika and Wolf, Matthias and Valadez, Gerardo Hermosillo},
	year = {2025},
	pages = {1017--1024},
	file = {Full Text PDF:/Users/xz/Zotero/storage/8RJI3R9G/Guo et al. - 2025 - Unsupervised Abnormality Segmentation in Chest CT with Anatomy-Guided Latent Diffusion Model and Ada.pdf:application/pdf},
}

@article{guo2024tai,
  title={TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction},
  author={Guo, Xueqi and Shi, Luyao and Chen, Xiongchao and Liu, Qiong and Zhou, Bo and Xie, Huidong and Liu, Yi-Hwa and Palyo, Richard and Miller, Edward J and Sinusas, Albert J and others},
  journal={Medical image analysis},
  volume={96},
  pages={103190},
  year={2024},
  publisher={Elsevier}
}

@inproceedings{guo2023tai,
  title={TAI-GAN: Temporally and Anatomically Informed GAN for early-to-late frame conversion in dynamic cardiac PET motion correction},
  author={Guo, Xueqi and Shi, Luyao and Chen, Xiongchao and Zhou, Bo and Liu, Qiong and Xie, Huidong and Liu, Yi-Hwa and Palyo, Richard and Miller, Edward J and Sinusas, Albert J and others},
  booktitle={International Workshop on Simulation and Synthesis in Medical Imaging},
  pages={64--74},
  year={2023},
  organization={Springer}
}