@misc{consortium_monai_2024,
    title = {{MONAI}: medical open network for {AI}},
    shorttitle = {Monai},
    url = {https://zenodo.org/records/13942962},
    abstract = {AI Toolkit for Healthcare Imaging},
    urldate = {2025-03-07},
    publisher = {Zenodo},
    author = {Consortium, Monai},
    month = oct,
    year = {2024},
    doi = {10.5281/zenodo.13942962},
}

@article{zhou_review_2021,
    title = {A review of deep learning in medical imaging: imaging traits, technology trends, case studies with progress highlights, and future promises},
    volume = {109},
    issn = {0018-9219},
    shorttitle = {A review of deep learning in medical imaging},
    url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10544772/},
    doi = {10.1109/JPROC.2021.3054390},
    abstract = {Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high performance computing. However, medical imaging presents unique challenges that confront deep learning approaches. In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues. We cover the topics of network architecture, sparse and noisy labels, federating learning, interpretability, uncertainty quantification, etc. Then, we present several case studies that are commonly found in clinical practice, including digital pathology and chest, brain, cardiovascular, and abdominal imaging. Rather than presenting an exhaustive literature survey, we instead describe some prominent research highlights related to these case study applications. We conclude with a discussion and presentation of promising future directions.},
    language = {en},
    number = {5},
    urldate = {2025-03-07},
    journal = {Proceedings of the Ieee. Institute of Electrical and Electronics Engineers},
    author = {Zhou, S. Kevin and Greenspan, Hayit and Davatzikos, Christos and Duncan, James S. and van Ginneken, Bram and Madabhushi, Anant and Prince, Jerry L. and Rueckert, Daniel and Summers, Ronald M.},
    month = may,
    year = {2021},
    pmid = {37786449},
    pmcid = {PMC10544772},
    pages = {820--838},
}
@inproceedings{verma_manifold_2019,
    series = {Proceedings of {Machine} {Learning} {Research}},
    title = {Manifold mixup: better representations by interpolating hidden states},
    volume = {97},
    shorttitle = {Manifold mixup},
    url = {http://proceedings.mlr.press/v97/verma19a.html},
    language = {en},
    urldate = {2025-03-07},
    booktitle = {Proceedings of the 36th {International} {Conference} on {Machine} {Learning}, {Icml} 2019, 9-15 {June} 2019, {Long} {Beach}, {California}, {Usa}},
    publisher = {PMLR},
    author = {Verma, Vikas and Lamb, Alex and Beckham, Christopher and Najafi, Amir and Mitliagkas, Ioannis and Lopez-Paz, David and Bengio, Yoshua},
    editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
    year = {2019},
    pages = {6438--6447},
}
@inproceedings{yun_cutmix_2019,
    title = {{CutMix}: regularization strategy to train strong classifiers with localizable features},
    shorttitle = {{CutMix}},
    doi = {10.1109/ICCV.2019.00612},
    language = {en},
    booktitle = {2019 {Ieee}/cvf {International} {Conference} on {Computer} {Vision}, {Iccv} 2019, {Seoul}, {Korea} (south), {October} 27 - {November} 2, 2019},
    publisher = {IEEE},
    author = {Yun, Sangdoo and Han, Dongyoon and Chun, Sanghyuk and Oh, Seong Joon and Yoo, Youngjoon and Choe, Junsuk},
    year = {2019},
    pages = {6022--6031},
}

@inproceedings{yun_cutmix_2019,
	title = {{CutMix}: regularization strategy to train strong classifiers with localizable features},
	shorttitle = {{CutMix}},
	doi = {10.1109/ICCV.2019.00612},
	language = {en},
	booktitle = {2019 {Ieee}/cvf {International} {Conference} on {Computer} {Vision}, {Iccv} 2019, {Seoul}, {Korea} (south), {October} 27 - {November} 2, 2019},
	publisher = {IEEE},
	author = {Yun, Sangdoo and Han, Dongyoon and Chun, Sanghyuk and Oh, Seong Joon and Yoo, Youngjoon and Choe, Junsuk},
	year = {2019},
	pages = {6022--6031},
}

@article{jafari-khouzani_dataset_2011,
	title = {Dataset of magnetic resonance images of nonepileptic subjects and temporal lobe epilepsy patients for validation of hippocampal segmentation techniques},
	volume = {9},
	issn = {1559-0089},
	doi = {10.1007/s12021-010-9096-4},
	abstract = {The hippocampus has become the focus of research in several neurodegenerative disorders. Automatic segmentation of this structure from magnetic resonance (MR) imaging scans of the brain facilitates this work. Segmentation techniques must be evaluated using a dataset of MR images with accurate hippocampal outlines generated manually. Manual segmentation is not a trivial task. Lack of a unique segmentation protocol and poor image quality are only two factors that have confounded the consistency required for comparative study. We have developed a publicly available dataset of T1-weighted (T1W) MR images of epileptic and nonepileptic subjects along with their hippocampal outlines to provide a means of evaluation of segmentation techniques. This dataset contains 50 T1W MR images, 40 epileptic and ten nonepileptic. All images were manually segmented by a widely used protocol. Twenty five images were selected for training and were provided with hippocampal labels. Twenty five other images were provided without labels for testing algorithms. The users are allowed to evaluate their generated labels for the test images using 11 segmentation similarity metrics. Using this dataset, we evaluated two segmentation algorithms, Brain Parser and Classifier Fusion and Labeling (CFL), trained by the training set. For Brain Parser, an average Dice coefficient of 0.64 was obtained with the testing set. For CFL, this value was 0.75. Such findings indicate a need for further improvement of segmentation algorithms in order to enhance reliability.},
	language = {en},
	number = {4},
	journal = {Neuroinformatics},
	author = {Jafari-Khouzani, Kourosh and Elisevich, Kost V. and Patel, Suresh and Soltanian-Zadeh, Hamid},
	month = dec,
	year = {2011},
	pmid = {21286946},
	pmcid = {PMC4501402},
	keywords = {Adolescent, Adult, Epilepsy, Temporal Lobe, Female, Hippocampus, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Pattern Recognition, Automated, Young Adult},
	pages = {335--346},
}

@article{frisoni_eadc-adni_2015,
	title = {The {EADC}-{ADNI} harmonized protocol for manual hippocampal segmentation on magnetic resonance: evidence of validity},
	volume = {11},
	issn = {1552-5279},
	shorttitle = {The {EADC}-{ADNI} harmonized protocol for manual hippocampal segmentation on magnetic resonance},
	doi = {10.1016/j.jalz.2014.05.1756},
	abstract = {BACKGROUND: An international Delphi panel has defined a harmonized protocol (HarP) for the manual segmentation of the hippocampus on MR. The aim of this study is to study the concurrent validity of the HarP toward local protocols, and its major sources of variance.
METHODS: Fourteen tracers segmented 10 Alzheimer's Disease Neuroimaging Initiative (ADNI) cases scanned at 1.5 T and 3T following local protocols, qualified for segmentation based on the HarP through a standard web-platform and resegmented following the HarP. The five most accurate tracers followed the HarP to segment 15 ADNI cases acquired at three time points on both 1.5 T and 3T.
RESULTS: The agreement among tracers was relatively low with the local protocols (absolute left/right ICC 0.44/0.43) and much higher with the HarP (absolute left/right ICC 0.88/0.89). On the larger set of 15 cases, the HarP agreement within (left/right ICC range: 0.94/0.95 to 0.99/0.99) and among tracers (left/right ICC range: 0.89/0.90) was very high. The volume variance due to different tracers was 0.9\% of the total, comparing favorably to variance due to scanner manufacturer (1.2), atrophy rates (3.5), hemispheric asymmetry (3.7), field strength (4.4), and significantly smaller than the variance due to atrophy (33.5\%, P {\textless} .001), and physiological variability (49.2\%, P {\textless} .001).
CONCLUSIONS: The HarP has high measurement stability compared with local segmentation protocols, and good reproducibility within and among human tracers. Hippocampi segmented with the HarP can be used as a reference for the qualification of human tracers and automated segmentation algorithms.},
	language = {en},
	number = {2},
	journal = {Alzheimer's \& Dementia: the Journal of the Alzheimer's Association},
	author = {Frisoni, Giovanni B. and Jack, Clifford R. and Bocchetta, Martina and Bauer, Corinna and Frederiksen, Kristian S. and Liu, Yawu and Preboske, Gregory and Swihart, Tim and Blair, Melanie and Cavedo, Enrica and Grothe, Michel J. and Lanfredi, Mariangela and Martinez, Oliver and Nishikawa, Masami and Portegies, Marileen and Stoub, Travis and Ward, Chadwich and Apostolova, Liana G. and Ganzola, Rossana and Wolf, Dominik and Barkhof, Frederik and Bartzokis, George and DeCarli, Charles and Csernansky, John G. and deToledo-Morrell, Leyla and Geerlings, Mirjam I. and Kaye, Jeffrey and Killiany, Ronald J. and Lehéricy, Stephane and Matsuda, Hiroshi and O'Brien, John and Silbert, Lisa C. and Scheltens, Philip and Soininen, Hilkka and Teipel, Stefan and Waldemar, Gunhild and Fellgiebel, Andreas and Barnes, Josephine and Firbank, Michael and Gerritsen, Lotte and Henneman, Wouter and Malykhin, Nikolai and Pruessner, Jens C. and Wang, Lei and Watson, Craig and Wolf, Henrike and deLeon, Mony and Pantel, Johannes and Ferrari, Clarissa and Bosco, Paolo and Pasqualetti, Patrizio and Duchesne, Simon and Duvernoy, Henri and Boccardi, Marina and {EADC-ADNI Working Group on The Harmonized Protocol for Manual Hippocampal Volumetry and for the Alzheimer's Disease Neuroimaging Initiative}},
	month = feb,
	year = {2015},
	pmid = {25267715},
	pmcid = {PMC4422168},
	keywords = {Aged, Alzheimer Disease, Alzheimer's disease, Atrophy, Biomarkers, Clinical trials, Diagnostic criteria, Enrichment, Female, Functional Laterality, Harmonized protocol, Hippocampal volumetry, Hippocampus, Humans, Image Processing, Computer-Assisted, Imaging, Three-Dimensional, Internet, Magnetic Resonance Imaging, Magnetic resonance, Male, Manual segmentation, Middle Aged, Neuroimaging, Organ Size, Reproducibility of Results, Standard operating procedures, Validation},
	pages = {111--125},
}

@inproceedings{verma_manifold_2019,
	series = {Proceedings of {Machine} {Learning} {Research}},
	title = {Manifold mixup: better representations by interpolating hidden states},
	volume = {97},
	shorttitle = {Manifold mixup},
	url = {http://proceedings.mlr.press/v97/verma19a.html},
	language = {en},
	urldate = {2025-03-07},
	booktitle = {Proceedings of the 36th {International} {Conference} on {Machine} {Learning}, {Icml} 2019, 9-15 {June} 2019, {Long} {Beach}, {California}, {Usa}},
	publisher = {PMLR},
	author = {Verma, Vikas and Lamb, Alex and Beckham, Christopher and Najafi, Amir and Mitliagkas, Ioannis and Lopez-Paz, David and Bengio, Yoshua},
	editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
	year = {2019},
	pages = {6438--6447},
}

@article{shen_object_2020,
	title = {Object detection from scratch with deep supervision},
	volume = {42},
	issn = {1939-3539},
	url = {https://ieeexplore.ieee.org/document/8734700},
	doi = {10.1109/TPAMI.2019.2922181},
	abstract = {In this paper, we propose Deeply Supervised Object Detectors (DSOD), an object detection framework that can be trained from scratch. Recent advances in object detection heavily depend on the off-the-shelf models pre-trained on large-scale classification datasets like ImageNet and OpenImage. However, one problem is that adopting pre-trained models from classification to detection task may incur learning bias due to the different objective function and diverse distributions of object categories. Techniques like fine-tuning on detection task could alleviate this issue to some extent but are still not fundamental. Furthermore, transferring these pre-trained models across discrepant domains will be more difficult (e.g., from RGB to depth images). Thus, a better solution to handle these critical problems is to train object detectors from scratch, which motivates our proposed method. Previous efforts on this direction mainly failed by reasons of the limited training data and naive backbone network structures for object detection. In DSOD, we contribute a set of design principles for learning object detectors from scratch. One of the key principles is the deep supervision, enabled by layer-wise dense connections in both backbone networks and prediction layers, plays a critical role in learning good detectors from scratch. After involving several other principles, we build our DSOD based on the single-shot detection framework (SSD). We evaluate our method on PASCAL VOC 2007, 2012 and COCO datasets. DSOD achieves consistently better results than the state-of-the-art methods with much more compact models. Specifically, DSOD outperforms baseline method SSD on all three benchmarks, while requiring only 1/2 parameters. We also observe that DSOD can achieve comparable/slightly better results than Mask RCNN [1] + FPN [2] (under similar input size) with only 1/3 parameters, using no extra data or pre-trained models.},
	language = {en},
	number = {2},
	urldate = {2025-03-07},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	author = {Shen, Zhiqiang and Liu, Zhuang and Li, Jianguo and Jiang, Yu-Gang and Chen, Yurong and Xue, Xiangyang},
	month = feb,
	year = {2020},
	note = {Conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence},
	keywords = {Computational modeling, Data models, Detectors, Linear programming, Object detection, Task analysis, Training, deeply supervised networks, densely connected layers, learning from scratch},
	pages = {398--412},
}

@article{geirhos_shortcut_2020,
	title = {Shortcut learning in deep neural networks},
	volume = {2},
	copyright = {2020 Springer Nature Limited},
	issn = {2522-5839},
	url = {https://www.nature.com/articles/s42256-020-00257-z},
	doi = {10.1038/s42256-020-00257-z},
	abstract = {Deep learning has triggered the current rise of artificial intelligence and is the workhorse of today’s machine intelligence. Numerous success stories have rapidly spread all over science, industry and society, but its limitations have only recently come into focus. In this Perspective we seek to distil how many of deep learning’s failures can be seen as different symptoms of the same underlying problem: shortcut learning. Shortcuts are decision rules that perform well on standard benchmarks but fail to transfer to more challenging testing conditions, such as real-world scenarios. Related issues are known in comparative psychology, education and linguistics, suggesting that shortcut learning may be a common characteristic of learning systems, biological and artificial alike. Based on these observations, we develop a set of recommendations for model interpretation and benchmarking, highlighting recent advances in machine learning to improve robustness and transferability from the lab to real-world applications.},
	language = {en},
	number = {11},
	urldate = {2025-03-07},
	journal = {Nature Machine Intelligence},
	author = {Geirhos, Robert and Jacobsen, Jörn-Henrik and Michaelis, Claudio and Zemel, Richard and Brendel, Wieland and Bethge, Matthias and Wichmann, Felix A.},
	month = nov,
	year = {2020},
	note = {Publisher: Nature Publishing Group},
	keywords = {Computational science, Human behaviour, Information technology, Network models},
	pages = {665--673},
}

@inproceedings{tsipras_robustness_2019,
	title = {Robustness may {Be} at odds with accuracy},
	url = {https://openreview.net/forum?id=SyxAb30cY7},
	language = {en},
	urldate = {2025-03-07},
	booktitle = {7th {International} {Conference} on {Learning} {Representations}, {Iclr} 2019, {New} {Orleans}, {La}, {Usa}, {May} 6-9, 2019},
	publisher = {OpenReview.net},
	author = {Tsipras, Dimitris and Santurkar, Shibani and Engstrom, Logan and Turner, Alexander and Madry, Aleksander},
	year = {2019},
}

@inproceedings{zhang_theoretically_2019,
	series = {Proceedings of {Machine} {Learning} {Research}},
	title = {Theoretically principled trade-off between robustness and accuracy},
	volume = {97},
	url = {http://proceedings.mlr.press/v97/zhang19p.html},
	language = {en},
	urldate = {2025-03-07},
	booktitle = {Proceedings of the 36th {International} {Conference} on {Machine} {Learning}, {Icml} 2019, 9-15 {June} 2019, {Long} {Beach}, {California}, {Usa}},
	publisher = {PMLR},
	author = {Zhang, Hongyang and Yu, Yaodong and Jiao, Jiantao and Xing, Eric P. and Ghaoui, Laurent El and Jordan, Michael I.},
	editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
	year = {2019},
	pages = {7472--7482},
}

@inproceedings{hendrycks_using_2019,
	series = {Proceedings of {Machine} {Learning} {Research}},
	title = {Using pre-training can improve model robustness and uncertainty},
	volume = {97},
	url = {http://proceedings.mlr.press/v97/hendrycks19a.html},
	language = {en},
	urldate = {2025-03-07},
	booktitle = {Proceedings of the 36th {International} {Conference} on {Machine} {Learning}, {Icml} 2019, 9-15 {June} 2019, {Long} {Beach}, {California}, {Usa}},
	publisher = {PMLR},
	author = {Hendrycks, Dan and Lee, Kimin and Mazeika, Mantas},
	editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
	year = {2019},
	pages = {2712--2721},
}

@article{zhou_review_2021,
	title = {A review of deep learning in medical imaging: imaging traits, technology trends, case studies with progress highlights, and future promises},
	volume = {109},
	issn = {0018-9219},
	shorttitle = {A review of deep learning in medical imaging},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10544772/},
	doi = {10.1109/JPROC.2021.3054390},
	abstract = {Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high performance computing. However, medical imaging presents unique challenges that confront deep learning approaches. In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues. We cover the topics of network architecture, sparse and noisy labels, federating learning, interpretability, uncertainty quantification, etc. Then, we present several case studies that are commonly found in clinical practice, including digital pathology and chest, brain, cardiovascular, and abdominal imaging. Rather than presenting an exhaustive literature survey, we instead describe some prominent research highlights related to these case study applications. We conclude with a discussion and presentation of promising future directions.},
	language = {en},
	number = {5},
	urldate = {2025-03-07},
	journal = {Proceedings of the Ieee. Institute of Electrical and Electronics Engineers},
	author = {Zhou, S. Kevin and Greenspan, Hayit and Davatzikos, Christos and Duncan, James S. and van Ginneken, Bram and Madabhushi, Anant and Prince, Jerry L. and Rueckert, Daniel and Summers, Ronald M.},
	month = may,
	year = {2021},
	pmid = {37786449},
	pmcid = {PMC10544772},
	pages = {820--838},
}

@misc{consortium_monai_2024,
	title = {{MONAI}: medical open network for {AI}},
	shorttitle = {Monai},
	url = {https://zenodo.org/records/13942962},
	abstract = {AI Toolkit for Healthcare Imaging},
	urldate = {2025-03-07},
	publisher = {Zenodo},
	author = {Consortium, Monai},
	month = oct,
	year = {2024},
	doi = {10.5281/zenodo.13942962},
}

@inproceedings{zhang_how_2021,
	title = {How does mixup help with robustness and generalization?},
	url = {https://openreview.net/forum?id=8yKEo06dKNo},
	doi = {arXiv.2010.04819},
	abstract = {Mixup is a popular data augmentation technique based on taking convex combinations of pairs of examples and their labels. This simple technique has been shown to substantially improve both the robustness and the generalization of the trained model. However, it is not well-understood why such improvement occurs. In this paper, we provide theoretical analysis to demonstrate how using Mixup in training helps model robustness and generalization. For robustness, we show that minimizing the Mixup loss corresponds to approximately minimizing an upper bound of the adversarial loss. This explains why models obtained by Mixup training exhibits robustness to several kinds of adversarial attacks such as Fast Gradient Sign Method (FGSM). For generalization, we prove that Mixup augmentation corresponds to a specific type of data-adaptive regularization which reduces overfitting. Our analysis provides new insights and a framework to understand Mixup.},
	language = {en},
	urldate = {2025-02-28},
	booktitle = {9th {International} {Conference} on {Learning} {Representations}, {Iclr} 2021, {Virtual} {Event}, {Austria}, {May} 3-7, 2021},
	publisher = {OpenReview.net},
	author = {Zhang, Linjun and Deng, Zhun and Kawaguchi, Kenji and Ghorbani, Amirata and Zou, James},
	year = {2021},
	keywords = {linter/error},
}

@misc{li_towards_2025,
	title = {Towards understanding why data augmentation improves generalization},
	url = {http://arxiv.org/abs/2502.08940},
	doi = {10.48550/arXiv.2502.08940},
	abstract = {Data augmentation is a cornerstone technique in deep learning, widely used to improve model generalization. Traditional methods like random cropping and color jittering, as well as advanced techniques such as CutOut, Mixup, and CutMix, have achieved notable success across various domains. However, the mechanisms by which data augmentation improves generalization remain poorly understood, and existing theoretical analyses typically focus on individual techniques without a unified explanation. In this work, we present a unified theoretical framework that elucidates how data augmentation enhances generalization through two key effects: partial semantic feature removal and feature mixing. Partial semantic feature removal reduces the model's reliance on individual feature, promoting diverse feature learning and better generalization. Feature mixing, by scaling down original semantic features and introducing noise, increases training complexity, driving the model to develop more robust features. Advanced methods like CutMix integrate both effects, achieving complementary benefits. Our theoretical insights are further supported by experimental results, validating the effectiveness of this unified perspective.},
	language = {en},
	urldate = {2025-02-27},
	publisher = {arXiv},
	author = {Li, Jingyang and Pan, Jiachun and Toh, Kim-Chuan and Zhou, Pan},
	month = feb,
	year = {2025},
	note = {arXiv:2502.08940 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning},
}

@inproceedings{modas_prime_2022,
	address = {Cham},
	title = {{PRIME}: a few primitives can boost robustness to common corruptions},
	isbn = {978-3-031-19806-9},
	shorttitle = {{PRIME}},
	url = {https://link.springer.com/chapter/10.1007/978-3-031-19806-9_36},
	doi = {10.1007/978-3-031-19806-9_36},
	abstract = {Despite their impressive performance on image classification tasks, deep networks have a hard time generalizing to unforeseen corruptions of their data. To fix this vulnerability, prior works have built complex data augmentation strategies, combining multiple methods to enrich the training data. However, introducing intricate design choices or heuristics makes it hard to understand which elements of these methods are indeed crucial for improving robustness. In this work, we take a step back and follow a principled approach to achieve robustness to common corruptions. We propose PRIME, a general data augmentation scheme that relies on simple yet rich families of max-entropy image transformations. PRIME outperforms the prior art in terms of corruption robustness, while its simplicity and plug-and-play nature enable combination with other methods to further boost their robustness. We analyze PRIME to shed light on the importance of the mixing strategy on synthesizing corrupted images, and to reveal the robustness-accuracy trade-offs arising in the context of common corruptions. Finally, we show that the computational efficiency of our method allows it to be easily used in both on-line and off-line data augmentation schemes. Our code is available at https://github.com/amodas/PRIME-augmentations.},
	language = {en},
	booktitle = {Computer {Vision} – {ECCV} 2022},
	publisher = {Springer Nature Switzerland},
	author = {Modas, Apostolos and Rade, Rahul and Ortiz-Jiménez, Guillermo and Moosavi-Dezfooli, Seyed-Mohsen and Frossard, Pascal},
	editor = {Avidan, Shai and Brostow, Gabriel and Cissé, Moustapha and Farinella, Giovanni Maria and Hassner, Tal},
	year = {2022},
	pages = {623--640},
}

@inproceedings{allen-zhu_towards_2023,
	title = {Towards understanding ensemble, knowledge distillation and self-distillation in deep learning},
	url = {https://openreview.net/forum?id=Uuf2q9TfXGA},
	abstract = {We formally study how ensemble of deep learning models can improve test accuracy, and how the superior performance of ensemble can be distilled into a single model using knowledge distillation. We consider the challenging case where the ensemble is simply an average of the outputs of a few independently trained neural networks with the SAME architecture, trained using the SAME algorithm on the SAME data set, and they only differ by the random seeds used in the initialization.
We show that ensemble/knowledge distillation in Deep Learning works very differently from traditional learning theory (such as boosting or NTKs, neural tangent kernels). To properly understand them, we develop a theory showing that when data has a structure we refer to as ``multi-view'', then ensemble of independently trained neural networks can provably improve test accuracy, and such superior test accuracy can also be provably distilled into a single model by training a single model to match the output of the ensemble instead of the true label. Our result sheds light on how ensemble works in deep learning in a way that is completely different from traditional theorems, and how the ``dark knowledge'' is hidden in the outputs of the ensemble and can be used in distillation. In the end, we prove that self-distillation can also be viewed as implicitly combining ensemble and knowledge distillation to improve test accuracy.},
	language = {en},
	urldate = {2025-02-27},
	booktitle = {The {Eleventh} {International} {Conference} on {Learning} {Representations}, {Iclr} 2023, {Kigali}, {Rwanda}, {May} 1-5, 2023},
	publisher = {OpenReview.net},
	author = {Allen-Zhu, Zeyuan and Li, Yuanzhi},
	year = {2023},
	keywords = {theory},
}

@article{sarnthein_random_2023,
	series = {Guide {Proceedings}},
	title = {Random teachers are good teachers},
	url = {https://dl.acm.org/doi/10.5555/3618408.3619654},
	doi = {10.5555/3618408.3619654},
	abstract = {In this work, we investigate the implicit regularization induced by teacher-student learning dynamics in self-distillation. To isolate its effect, we describe a simple experiment where we consider teachers at random initialization instead of trained teachers. Surprisingly, when distilling a student into such a random teacher, we observe that the resulting model and its representations already possess very interesting characteristics; (1) we observe a strong improvement of the distilled student over its teacher in terms of probing accuracy. (2) The learned representations are data-dependent and transferable between different tasks but deteriorate strongly if trained on random inputs. (3) The student checkpoint contains sparse subnetworks, so-called lottery tickets, and lies on the border of linear basins in the supervised loss landscape. These observations have interesting consequences for several important areas in machine learning: (1) Self-distillation can work solely based on the implicit regularization present in the gradient dynamics without relying on any dark knowledge, (2) self-supervised learning can learn features even in the absence of data augmentation, and (3) training dynamics during the early phase of supervised training do not necessarily require label information. Finally, we shed light on an intriguing local property of the loss landscape: the process of feature learning is strongly amplified if the student is initialized closely to the teacher. These results raise interesting questions about the nature of the landscape that have remained unexplored so far. Code is available at www.github.com/safelix/dinopl.},
	language = {en},
	urldate = {2025-02-27},
	journal = {Proceedings of the 40th International Conference on Machine Learning},
	author = {Sarnthein, Felix and {View Profile} and Bachmann, Gregor and {View Profile} and Anagnostidis, Sotiris and {View Profile} and Hofmann, Thomas and {View Profile}},
	month = jul,
	year = {2023},
	pages = {30022--30041},
}

@article{thulasidasan_mixup_2019,
	series = {Guide {Proceedings}},
	title = {On mixup training},
	url = {https://dl.acm.org/doi/10.5555/3454287.3455533},
	doi = {10.5555/3454287.3455533},
	abstract = {Mixup [37] is a recently proposed method for training deep neural networks where additional samples are generated during training by convexly combining random pairs of images and their associated labels. While simple to implement, it has shown to be a surprisingly effective method of data augmentation for image classification: DNNs trained with mixup show noticeable gains in classification performance on a number of image classification benchmarks. In this work, we discuss a hitherto untouched aspect of mixup training - the calibration and predictive uncertainty of models trained with mixup. We find that DNNs trained with mixup are significantly better calibrated - i.e the predicted softmax scores are much better indicators of the actual likelihood of a correct prediction - than DNNs trained in the regular fashion. We conduct experiments on a number of image classification architectures and datasets - including large-scale datasets like ImageNet - and find this to be the case. Additionally, we find that merely mixing features does not result in the same calibration benefit and that the label smoothing in mixup training plays a significant role in improving calibration. Finally, we also observe that mixup-trained DNNs are less prone to over-confident predictions on out-of-distribution and random-noise data. We conclude that the typical overconfidence seen in neural networks, even on in-distribution data is likely a consequence of training with hard labels, suggesting that mixup training be employed for classification tasks where predictive uncertainty is a significant concern.},
	language = {en},
	urldate = {2025-01-31},
	journal = {Proceedings of the 33rd International Conference on Neural Information Processing Systems},
	author = {Thulasidasan, Sunil and {View Profile} and Chennupati, Gopinath and {View Profile} and Bilmes, Jeff and {View Profile} and Bhattacharya, Tanmoy and {View Profile} and Michalak, Sarah and {View Profile}},
	month = dec,
	year = {2019},
	keywords = {augmentation},
	pages = {13911--13922},
}

@inproceedings{zhang_mixup_2018,
	title = {mixup: beyond empirical risk minimization},
	shorttitle = {mixup},
	url = {https://openreview.net/forum?id=r1Ddp1-Rb},
	abstract = {Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples. In this work, we propose mixup, a simple learning principle to alleviate these issues. In essence, mixup trains a neural network on convex combinations of pairs of examples and their labels.  By doing so, mixup regularizes the neural network to favor simple linear behavior in-between training examples.  Our experiments on the ImageNet-2012, CIFAR-10, CIFAR-100, Google commands and UCI datasets show that mixup improves the generalization of state-of-the-art neural network architectures.  We also find that mixup reduces the memorization of corrupt labels, increases the robustness to adversarial examples, and stabilizes the training of generative adversarial networks.},
	language = {en},
	urldate = {2025-02-27},
	booktitle = {6th {International} {Conference} on {Learning} {Representations}, {Iclr} 2018, {Vancouver}, {Bc}, {Canada}, {April} 30 - {May} 3, 2018, {Conference} {Track} {Proceedings}},
	publisher = {OpenReview.net},
	author = {Zhang, Hongyi and Cissé, Moustapha and Dauphin, Yann N. and Lopez-Paz, David},
	year = {2018},
	keywords = {augmentation},
}

@article{perez-garcia_torchio_2021,
	title = {{TorchIO}: a python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning},
	volume = {208},
	issn = {0169-2607},
	shorttitle = {{TorchIO}},
	url = {https://www.sciencedirect.com/science/article/pii/S0169260721003102},
	doi = {10.1016/j.cmpb.2021.106236},
	abstract = {Background and objective
Processing of medical images such as MRI or CT presents different challenges compared to RGB images typically used in computer vision. These include a lack of labels for large datasets, high computational costs, and the need of metadata to describe the physical properties of voxels. Data augmentation is used to artificially increase the size of the training datasets. Training with image subvolumes or patches decreases the need for computational power. Spatial metadata needs to be carefully taken into account in order to ensure a correct alignment and orientation of volumes.
Methods
We present TorchIO, an open-source Python library to enable efficient loading, preprocessing, augmentation and patch-based sampling of medical images for deep learning. TorchIO follows the style of PyTorch and integrates standard medical image processing libraries to efficiently process images during training of neural networks. TorchIO transforms can be easily composed, reproduced, traced and extended. Most transforms can be inverted, making the library suitable for test-time augmentation and estimation of aleatoric uncertainty in the context of segmentation. We provide multiple generic preprocessing and augmentation operations as well as simulation of MRI-specific artifacts.
Results
Source code, comprehensive tutorials and extensive documentation for TorchIO can be found at http://torchio.rtfd.io/. The package can be installed from the Python Package Index (PyPI) running pip install torchio. It includes a command-line interface which allows users to apply transforms to image files without using Python. Additionally, we provide a graphical user interface within a TorchIO extension in 3D Slicer to visualize the effects of transforms.
Conclusion
TorchIO was developed to help researchers standardize medical image processing pipelines and allow them to focus on the deep learning experiments. It encourages good open-science practices, as it supports experiment reproducibility and is version-controlled so that the software can be cited precisely. Due to its modularity, the library is compatible with other frameworks for deep learning with medical images.},
	language = {en},
	urldate = {2025-01-31},
	journal = {Computer Methods and Programs in Biomedicine},
	author = {Pérez-García, Fernando and Sparks, Rachel and Ourselin, Sébastien},
	month = sep,
	year = {2021},
	keywords = {Data augmentation, Deep learning, Medical image computing, Preprocessing},
	pages = {106236},
}

@article{cao_survey_2024,
	title = {A survey of mix-based data augmentation: taxonomy, methods, applications, and explainability},
	volume = {57},
	shorttitle = {A survey of mix-based data augmentation},
	url = {https://dl.acm.org/doi/10.1145/3696206},
	doi = {10.1145/3696206},
	abstract = {Data augmentation (DA) is indispensable in modern machine learning and deep neural networks. The basic idea of DA is to construct new training data to improve the model’s generalization by adding slightly disturbed versions of existing data or synthesizing new data. This survey comprehensively reviews a crucial subset of DA techniques, namely Mix-based Data Augmentation (MixDA), which generates novel samples by combining multiple examples. In contrast to traditional DA approaches that operate on single samples or entire datasets, MixDA stands out due to its effectiveness, simplicity, computational efficiency, theoretical foundation, and broad applicability. We begin by introducing a novel taxonomy that categorizes MixDA into Mixup-based, Cutmix-based, and mixture approaches based on a hierarchical perspective of the data mixing operation. Subsequently, we provide an in-depth review of various MixDA techniques, focusing on their underlying motivations. Owing to its versatility, MixDA has penetrated a wide range of applications, which we also thoroughly investigate in this survey. Moreover, we delve into the underlying mechanisms of MixDA’s effectiveness by examining its impact on model generalization and calibration while providing insights into the model’s behavior by analyzing the inherent properties of MixDA. Finally, we recapitulate the critical findings and fundamental challenges of current MixDA studies while outlining the potential directions for future works. Different from previous related surveys that focus on DA approaches in specific domains (e.g., computer vision and natural language processing) or only review a limited subset of MixDA studies, we are the first to provide a systematical survey of MixDA, covering its taxonomy, methodology, application, and explainability. Furthermore, we provide promising directions for researchers interested in this exciting area.},
	language = {en},
	number = {2},
	urldate = {2025-01-31},
	journal = {ACM Computing Surveys},
	author = {Cao, Chengtai and Zhou, Fan and Dai, Yurou and Wang, Jianping and Zhang, Kunpeng},
	month = oct,
	year = {2024},
	note = {Publisher: Association for Computing Machinery},
	keywords = {Data augmentation, deep learning, generalization, machine learning, regularization},
	pages = {1--38},
}

@inproceedings{liu_cut_2024,
	address = {Cham},
	title = {Cut to the mix: simple data augmentation outperforms elaborate ones in limited organ segmentation datasets},
	isbn = {978-3-031-72111-3},
	shorttitle = {Cut to the mix},
	doi = {10.1007/978-3-031-72111-3_14},
	abstract = {Multi-organ segmentation is a widely applied clinical routine and automated organ segmentation tools dramatically improve the pipeline of the radiologists. Recently, deep learning (DL) based segmentation models have shown the capacity to accomplish such a task. However, the training of the segmentation networks requires large amount of data with manual annotations, which is a major concern due to the data scarcity from clinic. Working with limited data is still common for researches on novel imaging modalities. To enhance the effectiveness of DL models trained with limited data, data augmentation (DA) is a crucial regularization technique. Traditional DA (TDA) strategies focus on basic intra-image operations, i.e. generating images with different orientations and intensity distributions. In contrast, the inter-image and object-level DA operations are able to create new images from separate individuals. However, such DA strategies are not well explored on the task of multi-organ segmentation. In this paper, we investigated four possible inter-image DA strategies: CutMix, CarveMix, ObjectAug and AnatoMix, on two organ segmentation datasets. The result shows that CutMix, CarveMix and AnatoMix can improve the average dice score by 4.9, 2.0 and 1.9, compared with the state-of-the-art nnUNet without DA strategies. These results can be further improved by adding TDA strategies. It is revealed in our experiments that CutMix is a robust but simple DA strategy to drive up the segmentation performance for multi-organ segmentation, even when CutMix produces intuitively ‘wrong’ images. Our implementation is publicly available at https://github.com/Rebooorn/mosDAtoolkitfor future benchmarks.},
	language = {en},
	booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2024},
	publisher = {Springer Nature Switzerland},
	author = {Liu, Chang and Fan, Fuxin and Schwarz, Annette and Maier, Andreas},
	editor = {Linguraru, Marius George and Dou, Qi and Feragen, Aasa and Giannarou, Stamatia and Glocker, Ben and Lekadir, Karim and Schnabel, Julia A.},
	year = {2024},
	keywords = {data augmentation, multi-organ segmentation},
	pages = {145--154},
}

@inproceedings{hendrycks_benchmarking_2019,
	title = {Benchmarking neural network robustness to common corruptions and perturbations},
	url = {http://arxiv.org/abs/1903.12261},
	doi = {10.48550/arXiv.1903.12261},
	abstract = {In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Then we propose a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations. Unlike recent robustness research, this benchmark evaluates performance on common corruptions and perturbations not worst-case adversarial perturbations. We find that there are negligible changes in relative corruption robustness from AlexNet classifiers to ResNet classifiers. Afterward we discover ways to enhance corruption and perturbation robustness. We even find that a bypassed adversarial defense provides substantial common perturbation robustness. Together our benchmarks may aid future work toward networks that robustly generalize.},
	language = {en},
	urldate = {2025-01-31},
	booktitle = {International {Conference} on {Learning} {Representations}},
	publisher = {arXiv},
	author = {Hendrycks, Dan and Dietterich, Thomas},
	month = mar,
	year = {2019},
	note = {arXiv:1903.12261 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning},
}

@inproceedings{eaton-rosen_improving_2018,
	title = {Improving data augmentation for medical image segmentation},
	url = {https://openreview.net/forum?id=rkBBChjiG},
	abstract = {Medical image segmentation is often constrained by the availability of labelled training data. ‘Data augmentation’ helps to prevent memorisation of training data and helps the network’s performance on data from outside the training set. As such, it is vital in building robust deep learning pipelines. Augmentation in medical imaging typically involves applying small transformations to images during training to create variety. However, it is also possible to use linear combinations of training images and labels to augment the dataset using the recently-proposed ‘mixup’ algorithm. Here, we apply this algorithm for use in medical imaging segmentation. We show that it increases performance in segmentation tasks, and also offer a theoretical suggestion for the efficacy of this technique.},
	language = {en},
	urldate = {2025-01-31},
	booktitle = {{MIDL} {Abstract}},
	author = {Eaton-Rosen, Zach and Bragman, Felix and Ourselin, Sebastien and Cardoso, M. Jorge},
	month = apr,
	year = {2018},
}

@article{xu_development_2023,
	title = {Development and clinical utility analysis of a prostate zonal segmentation model on {T2}-weighted imaging: a multicenter study},
	volume = {14},
	issn = {1869-4101},
	shorttitle = {Development and clinical utility analysis of a prostate zonal segmentation model on {T2}-weighted imaging},
	url = {https://doi.org/10.1186/s13244-023-01394-w},
	doi = {10.1186/s13244-023-01394-w},
	abstract = {To automatically segment prostate central gland (CG) and peripheral zone (PZ) on T2-weighted imaging using deep learning and assess the model’s clinical utility by comparing it with a radiologist annotation and analyzing relevant influencing factors, especially the prostate zonal volume.},
	language = {en},
	number = {1},
	urldate = {2025-01-31},
	journal = {Insights into Imaging},
	author = {Xu, Lili and Zhang, Gumuyang and Zhang, Daming and Zhang, Jiahui and Zhang, Xiaoxiao and Bai, Xin and Chen, Li and Peng, Qianyu and Jin, Ru and Mao, Li and Li, Xiuli and Jin, Zhengyu and Sun, Hao},
	month = mar,
	year = {2023},
	keywords = {Deep learning, Magnetic resonance imaging, Multicenter study, Prostate, Segmentation},
	pages = {44},
}

@inproceedings{vaish_fourier-basis_2024,
	address = {Los Alamitos, CA, USA},
	title = {Fourier-basis functions to bridge augmentation gap: rethinking frequency augmentation in image classification},
	url = {https://doi.ieeecomputersociety.org/10.1109/CVPR52733.2024.01682},
	doi = {10.1109/CVPR52733.2024.01682},
	abstract = {Computer vision models normally witness degraded performance when deployed in real-world scenarios, due to unexpected changes in inputs that were not accounted for during training. Data augmentation is commonly used to address this issue, as it aims to increase data variety and reduce the distribution gap between training and test data. However, common visual augmentations might not guaran-tee extensive robustness of computer vision models. In this paper, we propose Auxiliary Fourier-basis Augmentation (AFA), a complementary technique targeting augmentation in the frequency domain and filling the robustness gap left by visual augmentations. We demonstrate the utility of augmentation via Fourier-basis additive noise in a straightforward and efficient adversarial setting. Our results show that AFA benefits the robustness of models against common corruptions, OOD generalization, and consistency of performance of models against increasing perturbations, with negligible deficit to the standard performance of models. It can be seamlessly integrated with other augmentation techniques to further boost performance. Codes and models are available at https://github.com/nis-research/afa-augment.},
	language = {en},
	booktitle = {2024 {IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})},
	publisher = {IEEE Computer Society},
	author = {Vaish, Puru and Wang, Shunxin and Strisciuglio, Nicola},
	month = jun,
	year = {2024},
	keywords = {Computational modeling, Computer vision, Data augmentation, Frequency-domain analysis, Perturbation methods, Training, Visualization},
	pages = {17763--17772},
}

@article{situmorang_prostate_2012,
	title = {Prostate cancer in younger and older patients: do we treat them differently?},
	volume = {13},
	issn = {2476-762X},
	shorttitle = {Prostate cancer in younger and older patients},
	doi = {10.7314/apjcp.2012.13.9.4577},
	abstract = {Diagnostic and therapeutic strategies of prostate cancer may largely influenced by patients' age at presentation. This study is aimed to evaluate the characteristics, diagnostic and treatment strategies in prostate cancer patients in our centres. A cross-sectional analytic study of prostate cancer data in two main referral cancer centres, Cipto Mangunkusumo General Hospital and Dharmais National Cancer Centre from 1995-2010, was therefore performed. Patients were divided into 2 sub-populations; below 60 years (younger patients) and 75 years old and above (older patients). PSA levels, diagnostic modalities, Gleason score and therapeutic options were analysed for both and compared using bivariate analysis. 152 patients were {\textless}60 years and 210 were ≥75 years. There was no statistical difference in mean PSA level (797.9ng/mL vs 345.3 ng/mL, respectively; p{\textgreater}0.05) and diagnosis was made by biopsy in majority of patients in both groups (68.2\% and 71.6\% in younger and older groups respectively). Most presented with an advanced disease stage (65.1\% and 66.0\%, respectively) and there was no statistically significant difference in mean Gleason scores f (8.1 vs 7.7; p{\textgreater}0.05). Primary androgen deprivation therapy (PADT) was the main treatment for overall patients (48.0\% and 50.7\%, respectively). Radiotherapy and radical prostatectomy are the main therapeutic modalities for younger patients with local and locally advanced disease (39.6\% and 35.4\% respectively), while the majority of older patients with the same disease stage were treated with radiotherapy and PADT (45.8\% and 39.0\% respectively). Differences observed in treatment modalities were statistically significant (p{\textless}0.0003). We conclude that there is no difference in disease clinical aggressiveness of the two groups but significant differences were obseved in therapeutic strategies utilised with younger and older patients.},
	language = {en},
	number = {9},
	journal = {Asian Pacific journal of cancer prevention: APJCP},
	author = {Situmorang, Gerhard Reinaldi and Umbas, Rainy and Mochtar, Chaidir A. and Santoso, Rachmat Budi},
	year = {2012},
	pmid = {23167383},
	keywords = {Age Factors, Aged, Androgen Antagonists, Antineoplastic Agents, Biopsy, Cross-Sectional Studies, Humans, Indonesia, Male, Middle Aged, Neoplasm Grading, Neoplasm Staging, Prostate-Specific Antigen, Prostatectomy, Prostatic Neoplasms, Radiotherapy, Watchful Waiting},
	pages = {4577--4580},
}

@article{shimron_implicit_2022,
	title = {Implicit data crimes: {Machine} learning bias arising from misuse of public data},
	volume = {119},
	shorttitle = {Implicit data crimes},
	url = {https://www.pnas.org/doi/full/10.1073/pnas.2117203119},
	doi = {10.1073/pnas.2117203119},
	abstract = {Although open databases are an important resource in the current deep learning (DL) era, they are sometimes used “off label”: Data published for one task are used to train algorithms for a different one. This work aims to highlight that this common practice may lead to biased, overly optimistic results. We demonstrate this phenomenon for inverse problem solvers and show how their biased performance stems from hidden data-processing pipelines. We describe two processing pipelines typical of open-access databases and study their effects on three well-established algorithms developed for MRI reconstruction: compressed sensing, dictionary learning, and DL. Our results demonstrate that all these algorithms yield systematically biased results when they are naively trained on seemingly appropriate data: The normalized rms error improves consistently with the extent of data processing, showing an artificial improvement of 25 to 48\% in some cases. Because this phenomenon is not widely known, biased results sometimes are published as state of the art; we refer to that as implicit “data crimes.” This work hence aims to raise awareness regarding naive off-label usage of big data and reveal the vulnerability of modern inverse problem solvers to the resulting bias.},
	number = {13},
	urldate = {2025-01-31},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Shimron, Efrat and Tamir, Jonathan I. and Wang, Ke and Lustig, Michael},
	month = mar,
	year = {2022},
	note = {Publisher: Proceedings of the National Academy of Sciences},
	pages = {e2117203119},
}

@inproceedings{chuang_measuring_2021,
	title = {Measuring generalization with optimal transport},
	volume = {34},
	url = {https://proceedings.neurips.cc/paper/2021/hash/4607f7fff0dce694258e1c637512aa9d-Abstract.html},
	abstract = {Understanding the generalization of deep neural networks is one of the most important tasks in deep learning. Although much progress has been made, theoretical error bounds still often behave disparately from empirical observations. In this work, we develop margin-based generalization bounds, where the margins are normalized with optimal transport costs between independent random subsets sampled from the training distribution. In particular, the optimal transport cost can be interpreted as a generalization of variance which captures the structural properties of the learned feature space. Our bounds robustly predict the generalization error, given training data and network parameters, on large scale datasets. Theoretically, we demonstrate that the concentration and separation of features play crucial roles in generalization, supporting empirical results in the literature.},
	language = {en},
	urldate = {2025-01-31},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher = {Curran Associates, Inc.},
	author = {Chuang, Ching-Yao and Mroueh, Youssef and Greenewald, Kristjan and Torralba, Antonio and Jegelka, Stefanie},
	year = {2021},
	pages = {8294--8306},
}

@article{kamann_benchmarking_2021,
	title = {Benchmarking the robustness of semantic segmentation models with respect to common corruptions},
	volume = {129},
	issn = {1573-1405},
	url = {https://doi.org/10.1007/s11263-020-01383-2},
	doi = {10.1007/s11263-020-01383-2},
	abstract = {When designing a semantic segmentation model for a real-world application, such as autonomous driving, it is crucial to understand the robustness of the network with respect to a wide range of image corruptions. While there are recent robustness studies for full-image classification, we are the first to present an exhaustive study for semantic segmentation, based on many established neural network architectures. We utilize almost 400,000 images generated from the Cityscapes dataset, PASCAL VOC 2012, and ADE20K. Based on the benchmark study, we gain several new insights. Firstly, many networks perform well with respect to real-world image corruptions, such as a realistic PSF blur. Secondly, some architecture properties significantly affect robustness, such as a Dense Prediction Cell, designed to maximize performance on clean data only. Thirdly, the generalization capability of semantic segmentation models depends strongly on the type of image corruption. Models generalize well for image noise and image blur, however, not with respect to digitally corrupted data or weather corruptions.},
	language = {en},
	number = {2},
	urldate = {2025-01-31},
	journal = {International Journal of Computer Vision},
	author = {Kamann, Christoph and Rother, Carsten},
	month = feb,
	year = {2021},
	keywords = {Artificial Intelligence, Common image corruptions, Corruption robustness, Realistic image corruptions, Semantic segmentation},
	pages = {462--483},
}

@article{isensee_nnu-net_2021,
	title = {{nnU}-net: a self-configuring method for deep learning-based biomedical image segmentation},
	volume = {18},
	copyright = {2020 The Author(s), under exclusive licence to Springer Nature America, Inc.},
	issn = {1548-7105},
	shorttitle = {{nnU}-net},
	url = {https://www.nature.com/articles/s41592-020-01008-z},
	doi = {10.1038/s41592-020-01008-z},
	abstract = {Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation algorithms enable image analysis and quantification in many applications, the design of respective specialized solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We developed nnU-Net, a deep learning-based segmentation method that automatically configures itself, including preprocessing, network architecture, training and post-processing for any new task. The key design choices in this process are modeled as a set of fixed parameters, interdependent rules and empirical decisions. Without manual intervention, nnU-Net surpasses most existing approaches, including highly specialized solutions on 23 public datasets used in international biomedical segmentation competitions. We make nnU-Net publicly available as an out-of-the-box tool, rendering state-of-the-art segmentation accessible to a broad audience by requiring neither expert knowledge nor computing resources beyond standard network training.},
	language = {en},
	number = {2},
	urldate = {2025-01-31},
	journal = {Nature Methods},
	author = {Isensee, Fabian and Jaeger, Paul F. and Kohl, Simon A. A. and Petersen, Jens and Maier-Hein, Klaus H.},
	month = feb,
	year = {2021},
	note = {Publisher: Nature Publishing Group},
	keywords = {Image processing, Translational research},
	pages = {203--211},
}

@article{goceri_medical_2023,
	title = {Medical image data augmentation: techniques, comparisons and interpretations},
	volume = {56},
	issn = {1573-7462},
	shorttitle = {Medical image data augmentation},
	url = {https://doi.org/10.1007/s10462-023-10453-z},
	doi = {10.1007/s10462-023-10453-z},
	abstract = {Designing deep learning based methods with medical images has always been an attractive area of research to assist clinicians in rapid examination and accurate diagnosis. Those methods need a large number of datasets including all variations in their training stages. On the other hand, medical images are always scarce due to several reasons, such as not enough patients for some diseases, patients do not want to allow their images to be used, lack of medical equipment or equipment, inability to obtain images that meet the desired criteria. This issue leads to bias in datasets, overfitting, and inaccurate results. Data augmentation is a common solution to overcome this issue and various augmentation techniques have been applied to different types of images in the literature. However, it is not clear which data augmentation technique provides more efficient results for which image type since different diseases are handled, different network architectures are used, and these architectures are trained and tested with different numbers of data sets in the literature. Therefore, in this work, the augmentation techniques used to improve performances of deep learning based diagnosis of the diseases in different organs (brain, lung, breast, and eye) from different imaging modalities (MR, CT, mammography, and fundoscopy) have been examined. Also, the most commonly used augmentation methods have been implemented, and their effectiveness in classifications with a deep network has been discussed based on quantitative performance evaluations. Experiments indicated that augmentation techniques should be chosen carefully according to image types.},
	language = {en},
	number = {11},
	urldate = {2025-01-31},
	journal = {Artificial Intelligence Review},
	author = {Goceri, Evgin},
	month = nov,
	year = {2023},
	keywords = {Artificial Intelligence, Data augmentation, GAN, Medical Imaging, Medical images, Synthesis},
	pages = {12561--12605},
}

@inproceedings{gazda_mixup_2022,
	address = {Cham},
	title = {Mixup augmentation for kidney and kidney tumor segmentation},
	isbn = {978-3-030-98385-7},
	doi = {10.1007/978-3-030-98385-7_12},
	abstract = {Abdominal computed tomography is frequently used to non-invasively map local conditions and to detect any benign or malign masses. However, ill-defined borders of malign objects, fuzzy texture, and time pressure in fact, make accurate segmentation in clinical settings a challenging task. In this paper, we propose a two-stage deep learning architecture for kidney and kidney masses segmentation, denoted as convolutional computer tomography network (CCTNet). The first stage locates volume bounding box containing both kidneys. The second stage performs the segmentation of kidney, kidney tumors and cysts. In the first stage, we use a pre-trained 3D low resolution nnU-Net. In the second stage, we employ a mixup augmentation to improve segmentation performance of the second 3D full resolution nnU-Net. The obtained results indicate that CCTNet can provide improved segmentation of kidney, kidney tumor and cyst.},
	language = {en},
	booktitle = {Kidney and {Kidney} {Tumor} {Segmentation}},
	publisher = {Springer International Publishing},
	author = {Gazda, Matej and Bugata, Peter and Gazda, Jakub and Hubacek, David and Hresko, David Jozef and Drotar, Peter},
	editor = {Heller, Nicholas and Isensee, Fabian and Trofimova, Darya and Tejpaul, Resha and Papanikolopoulos, Nikolaos and Weight, Christopher},
	year = {2022},
	keywords = {CT, Kidney, Mixup, Segmentation, nnU-Net},
	pages = {90--97},
}

@article{garcea_data_2023,
	title = {Data augmentation for medical imaging: a systematic literature review},
	volume = {152},
	issn = {0010-4825},
	shorttitle = {Data augmentation for medical imaging},
	url = {https://www.sciencedirect.com/science/article/pii/S001048252201099X},
	doi = {10.1016/j.compbiomed.2022.106391},
	abstract = {Recent advances in Deep Learning have largely benefited from larger and more diverse training sets. However, collecting large datasets for medical imaging is still a challenge due to privacy concerns and labeling costs. Data augmentation makes it possible to greatly expand the amount and variety of data available for training without actually collecting new samples. Data augmentation techniques range from simple yet surprisingly effective transformations such as cropping, padding, and flipping, to complex generative models. Depending on the nature of the input and the visual task, different data augmentation strategies are likely to perform differently. For this reason, it is conceivable that medical imaging requires specific augmentation strategies that generate plausible data samples and enable effective regularization of deep neural networks. Data augmentation can also be used to augment specific classes that are underrepresented in the training set, e.g., to generate artificial lesions. The goal of this systematic literature review is to investigate which data augmentation strategies are used in the medical domain and how they affect the performance of clinical tasks such as classification, segmentation, and lesion detection. To this end, a comprehensive analysis of more than 300 articles published in recent years (2018–2022) was conducted. The results highlight the effectiveness of data augmentation across organs, modalities, tasks, and dataset sizes, and suggest potential avenues for future research.},
	language = {en},
	urldate = {2025-01-31},
	journal = {Computers in Biology and Medicine},
	author = {Garcea, Fabio and Serra, Alessio and Lamberti, Fabrizio and Morra, Lia},
	month = jan,
	year = {2023},
	keywords = {Data augmentation, Deep learning, Generative adversarial networks, MRI, Medical imaging},
	pages = {106391},
}

@article{campello_multi-centre_2021,
	title = {Multi-centre, multi-vendor and multi-disease cardiac segmentation: the {M}\&ms challenge},
	volume = {40},
	issn = {1558-254X},
	shorttitle = {Multi-centre, multi-vendor and multi-disease cardiac segmentation},
	url = {https://ieeexplore.ieee.org/document/9458279},
	doi = {10.1109/TMI.2021.3090082},
	abstract = {The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M\&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field.},
	language = {en},
	number = {12},
	urldate = {2025-01-31},
	journal = {IEEE Transactions on Medical Imaging},
	author = {Campello, Víctor M. and Gkontra, Polyxeni and Izquierdo, Cristian and Martín-Isla, Carlos and Sojoudi, Alireza and Full, Peter M. and Maier-Hein, Klaus and Zhang, Yao and He, Zhiqiang and Ma, Jun and Parreño, Mario and Albiol, Alberto and Kong, Fanwei and Shadden, Shawn C. and Acero, Jorge Corral and Sundaresan, Vaanathi and Saber, Mina and Elattar, Mustafa and Li, Hongwei and Menze, Bjoern and Khader, Firas and Haarburger, Christoph and Scannell, Cian M. and Veta, Mitko and Carscadden, Adam and Punithakumar, Kumaradevan and Liu, Xiao and Tsaftaris, Sotirios A. and Huang, Xiaoqiong and Yang, Xin and Li, Lei and Zhuang, Xiahai and Viladés, David and Descalzo, Martín L. and Guala, Andrea and Mura, Lucia La and Friedrich, Matthias G. and Garg, Ria and Lebel, Julie and Henriques, Filipe and Karakas, Mahir and Çavuş, Ersin and Petersen, Steffen E. and Escalera, Sergio and Seguí, Santi and Rodríguez-Palomares, José F. and Lekadir, Karim},
	month = dec,
	year = {2021},
	note = {Conference Name: IEEE Transactions on Medical Imaging},
	keywords = {Biomedical engineering, Cardiovascular magnetic resonance, Deep learning, Heart, Hospitals, Image segmentation, Protocols, Training, data augmentation, deep learning, domain adaption, generalizability, image segmentation, public dataset},
	pages = {3543--3554},
}

@article{boone_rood-mri_2023,
	title = {{ROOD}-{MRI}: benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in {MRI}},
	volume = {278},
	issn = {1053-8119},
	shorttitle = {Rood-mri},
	url = {https://www.sciencedirect.com/science/article/pii/S1053811923004408},
	doi = {10.1016/j.neuroimage.2023.120289},
	abstract = {Deep artificial neural networks (DNNs) have moved to the forefront of medical image analysis due to their success in classification, segmentation, and detection challenges. A principal challenge in large-scale deployment of DNNs in neuroimage analysis is the potential for shifts in signal-to-noise ratio, contrast, resolution, and presence of artifacts from site to site due to variances in scanners and acquisition protocols. DNNs are famously susceptible to these distribution shifts in computer vision. Currently, there are no benchmarking platforms or frameworks to assess the robustness of new and existing models to specific distribution shifts in MRI, and accessible multi-site benchmarking datasets are still scarce or task-specific. To address these limitations, we propose ROOD-MRI: a novel platform for benchmarking the Robustness of DNNs to Out-Of-Distribution (OOD) data, corruptions, and artifacts in MRI. This flexible platform provides modules for generating benchmarking datasets using transforms that model distribution shifts in MRI, implementations of newly derived benchmarking metrics for image segmentation, and examples for using the methodology with new models and tasks. We apply our methodology to hippocampus, ventricle, and white matter hyperintensity segmentation in several large studies, providing the hippocampus dataset as a publicly available benchmark. By evaluating modern DNNs on these datasets, we demonstrate that they are highly susceptible to distribution shifts and corruptions in MRI. We show that while data augmentation strategies can substantially improve robustness to OOD data for anatomical segmentation tasks, modern DNNs using augmentation still lack robustness in more challenging lesion-based segmentation tasks. We finally benchmark U-Nets and vision transformers, finding robustness susceptibility to particular classes of transforms across architectures. The presented open-source platform enables generating new benchmarking datasets and comparing across models to study model design that results in improved robustness to OOD data and corruptions in MRI.},
	language = {en},
	urldate = {2025-01-31},
	journal = {Neuroimage},
	author = {Boone, Lyndon and Biparva, Mahdi and Mojiri Forooshani, Parisa and Ramirez, Joel and Masellis, Mario and Bartha, Robert and Symons, Sean and Strother, Stephen and Black, Sandra E. and Heyn, Chris and Martel, Anne L. and Swartz, Richard H. and Goubran, Maged},
	month = sep,
	year = {2023},
	keywords = {Benchmarking, Corruptions and artifacts, Generalizable deep learning, MRI, Out-of-distribution data, Segmentation},
	pages = {120289},
}

@article{bernard_deep_2018,
	title = {Deep learning techniques for automatic {MRI} cardiac multi-structures segmentation and diagnosis: is the problem solved?},
	volume = {37},
	issn = {1558-254X},
	shorttitle = {Deep learning techniques for automatic {MRI} cardiac multi-structures segmentation and diagnosis},
	url = {https://ieeexplore.ieee.org/document/8360453},
	doi = {10.1109/TMI.2018.2837502},
	abstract = {Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the “Automatic Cardiac Diagnosis Challenge” dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions.},
	language = {en},
	number = {11},
	urldate = {2025-01-31},
	journal = {IEEE Transactions on Medical Imaging},
	author = {Bernard, Olivier and Lalande, Alain and Zotti, Clement and Cervenansky, Frederick and Yang, Xin and Heng, Pheng-Ann and Cetin, Irem and Lekadir, Karim and Camara, Oscar and Gonzalez Ballester, Miguel Angel and Sanroma, Gerard and Napel, Sandy and Petersen, Steffen and Tziritas, Georgios and Grinias, Elias and Khened, Mahendra and Kollerathu, Varghese Alex and Krishnamurthi, Ganapathy and Rohé, Marc-Michel and Pennec, Xavier and Sermesant, Maxime and Isensee, Fabian and Jäger, Paul and Maier-Hein, Klaus H. and Full, Peter M. and Wolf, Ivo and Engelhardt, Sandy and Baumgartner, Christian F. and Koch, Lisa M. and Wolterink, Jelmer M. and Išgum, Ivana and Jang, Yeonggul and Hong, Yoonmi and Patravali, Jay and Jain, Shubham and Humbert, Olivier and Jodoin, Pierre-Marc},
	month = nov,
	year = {2018},
	note = {Conference Name: IEEE Transactions on Medical Imaging},
	keywords = {Biomedical imaging, Cardiac segmentation and diagnosis, Heart, Image segmentation, MRI, Machine learning, Magnetic resonance imaging, Myocardium, Task analysis, deep learning, left and right ventricles, myocardium},
	pages = {2514--2525},
}

@inproceedings{atya_non_2021,
	title = {Non parametric data augmentations improve deep-learning based brain tumor segmentation},
	url = {https://ieeexplore.ieee.org/document/9629083},
	doi = {10.1109/COMCAS52219.2021.9629083},
	abstract = {Automatic brain tumor segmentation from Magnetic Resonance Imaging (MRI) data plays an important role in assessing tumor response to therapy and personalized treatment stratification. Manual segmentation is tedious and subjective. Deep-learning based algorithms for brain tumor segmentation have the potential to provide objective and fast tumor segmentation. However, the training of such algorithms requires large datasets which are not always available. Data augmentation techniques may reduce the need for large datasets. However current approaches are mostly parametric and may result in suboptimal performance. We introduce two non-parametric methods of data augmentation for brain tumor segmentation: the mixed structure regularization (MSR) and shuffle pixels noise (SPN). We evaluated the added value of the MSR and SPN augmentation on the brain tumor segmentation (BraTS) 2018 challenge dataset with the encoder-decoder nnU-Net architecture as the segmentation algorithm. Both MSR ans SPN improve the nnU-Net segmentation accuracy compared to parametric Gaussian noise augmentation.(Mean dice score increased from 80\% to 82\% and p-values=0.0022, 0.0028 when comparing MSR to non parametric augmentation for the tumor core and whole tumor experiments respectively. The proposed MSR and SPN augmentations has the potential to improve neural-networks performance in other tasks as well.},
	language = {en},
	urldate = {2025-01-31},
	booktitle = {2021 {IEEE} {International} {Conference} on {Microwaves}, {Antennas}, {Communications} and {Electronic} {Systems} ({COMCAS})},
	author = {Atya, Hadas Ben and Rajchert, Ori and Goshen, Liran and Freiman, Moti},
	month = nov,
	year = {2021},
	keywords = {Artificial neural networks, Brain Tumor Segmentation, Data Augmentation, Image segmentation, Magnetic resonance imaging, Medical Image Segmentation, Microwave antennas, Microwave communication, Microwave theory and techniques, Training, nnU-Net},
	pages = {357--360},
}

@article{armato_prostatex_2018,
	title = {{PROSTATEx} challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images},
	volume = {5},
	issn = {2329-4302},
	doi = {10.1117/1.JMI.5.4.044501},
	abstract = {Grand challenges stimulate advances within the medical imaging research community; within a competitive yet friendly environment, they allow for a direct comparison of algorithms through a well-defined, centralized infrastructure. The tasks of the two-part PROSTATEx Challenges (the PROSTATEx Challenge and the PROSTATEx-2 Challenge) are (1) the computerized classification of clinically significant prostate lesions and (2) the computerized determination of Gleason Grade Group in prostate cancer, both based on multiparametric magnetic resonance images. The challenges incorporate well-vetted cases for training and testing, a centralized performance assessment process to evaluate results, and an established infrastructure for case dissemination, communication, and result submission. In the PROSTATEx Challenge, 32 groups apply their computerized methods (71 methods total) to 208 prostate lesions in the test set. The area under the receiver operating characteristic curve for these methods in the task of differentiating between lesions that are and are not clinically significant ranged from 0.45 to 0.87; statistically significant differences in performance among the top-performing methods, however, are not observed. In the PROSTATEx-2 Challenge, 21 groups apply their computerized methods (43 methods total) to 70 prostate lesions in the test set. When compared with the reference standard, the quadratic-weighted kappa values for these methods in the task of assigning a five-point Gleason Grade Group to each lesion range from - 0.24  to 0.27; superiority to random guessing can be established for only two methods. When approached with a sense of commitment and scientific rigor, challenges foster interest in the designated task and encourage innovation in the field.},
	language = {en},
	number = {4},
	journal = {Journal of Medical Imaging (Bellingham, Wash.)},
	author = {Armato, Samuel G. and Huisman, Henkjan and Drukker, Karen and Hadjiiski, Lubomir and Kirby, Justin S. and Petrick, Nicholas and Redmond, George and Giger, Maryellen L. and Cha, Kenny and Mamonov, Artem and Kalpathy-Cramer, Jayashree and Farahani, Keyvan},
	month = oct,
	year = {2018},
	pmid = {30840739},
	pmcid = {PMC6228312},
	keywords = {Gleason Grade Group, grand challenge, imaging biomarker, lesion classification, multiparametric magnetic resonance images, prostate cancer},
	pages = {44501},
}

@article{allen_age-related_1989,
	title = {Age-related changes of the prostate: evaluation by {MR} imaging},
	volume = {152},
	issn = {0361-803X},
	shorttitle = {Age-related changes of the prostate},
	url = {https://www.ajronline.org/doi/10.2214/ajr.152.1.77},
	doi = {10.2214/ajr.152.1.77},
	abstract = {The pelvic MR examinations of 40 men without known prostatic disease were reviewed retrospectively. Axial long TR/long TE images were evaluated with respect to prostatic zonal size and signal intensity. Findings were correlated with each patient's age (17-74 years). The central region of the prostate and the peripheral zone enlarged with age; the central gland increased in size by an average of 175\% between the second and eighth decades and the peripheral zone increased by an average of 67\%. The anterior fibromuscular stroma decreased with increasing age (from an average anteroposterior thickness of 1.2 cm in the second decade to 0.4 cm in the eighth decade) and also became thinner as a function of increasing gland size. The periprostatic venous plexus became less prominent as a function of increasing age, decreasing from 2.5 to 1.5 mm in average maximal diameter, but this venous caliber was not significantly correlated with gland size. The conspicuity of the peripheral zone with respect to the central gland was improved, both as a function of increasing age and increasing gland size, and conspicuity was greatest on long TR/long TE images because of excellent contrast resolution. We conclude that in older age groups, the zonal anatomy of the prostate is more clearly defined than in young patients, both because of morphologic changes in prostate structure and because of physiologic changes resulting in differing zonal MR signal intensities.},
	language = {en},
	number = {1},
	urldate = {2025-01-31},
	journal = {American Journal of Roentgenology},
	author = {Allen, Ks and Kressel, Hy and Arger, Ph and Pollack, Hm},
	month = jan,
	year = {1989},
	note = {Publisher: American Roentgen Ray Society},
	pages = {77--81},
}

@article{adams_prostate158_2022,
	title = {Prostate158 - an expert-annotated {3T} {MRI} dataset and algorithm for prostate cancer detection},
	volume = {148},
	issn = {0010-4825},
	url = {https://www.sciencedirect.com/science/article/pii/S0010482522005789},
	doi = {10.1016/j.compbiomed.2022.105817},
	abstract = {Background
The development of deep learning (DL) models for prostate segmentation on magnetic resonance imaging (MRI) depends on expert-annotated data and reliable baselines, which are often not publicly available. This limits both reproducibility and comparability.
Methods
Prostate158 consists of 158 expert annotated biparametric 3T prostate MRIs comprising T2w sequences and diffusion-weighted sequences with apparent diffusion coefficient maps. Two U-ResNets trained for segmentation of anatomy (central gland, peripheral zone) and suspicious lesions for prostate cancer (PCa) with a PI-RADS score of ≥4 served as baseline algorithms. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), the Hausdorff distance (HD), and the average surface distance (ASD). The Wilcoxon test with Bonferroni correction was used to evaluate differences in performance. The generalizability of the baseline model was assessed using the open datasets Medical Segmentation Decathlon and PROSTATEx.
Results
Compared to Reader 1, the models achieved a DSC/HD/ASD of 0.88/18.3/2.2 for the central gland, 0.75/22.8/1.9 for the peripheral zone, and 0.45/36.7/17.4 for PCa. Compared with Reader 2, the DSC/HD/ASD were 0.88/17.5/2.6 for the central gland, 0.73/33.2/1.9 for the peripheral zone, and 0.4/39.5/19.1 for PCa. Interrater agreement measured in DSC/HD/ASD was 0.87/11.1/1.0 for the central gland, 0.75/15.8/0.74 for the peripheral zone, and 0.6/18.8/5.5 for PCa. Segmentation performances on the Medical Segmentation Decathlon and PROSTATEx were 0.82/22.5/3.4; 0.86/18.6/2.5 for the central gland, and 0.64/29.2/4.7; 0.71/26.3/2.2 for the peripheral zone.
Conclusions
We provide an openly accessible, expert-annotated 3T dataset of prostate MRI and a reproducible benchmark to foster the development of prostate segmentation algorithms.},
	language = {en},
	urldate = {2025-01-31},
	journal = {Computers in Biology and Medicine},
	author = {Adams, Lisa C. and Makowski, Marcus R. and Engel, Günther and Rattunde, Maximilian and Busch, Felix and Asbach, Patrick and Niehues, Stefan M. and Vinayahalingam, Shankeeth and van Ginneken, Bram and Litjens, Geert and Bressem, Keno K.},
	month = sep,
	year = {2022},
	keywords = {Artificial intelligence, Biparametric prostate MRI, Deep learning, Machine learning, Magnetic resonance imaging, Prostate cancer},
	pages = {105817},
}
