@inproceedings{Dupont2022FromDT,
	author = {Dupont, Emilien and Kim, Hyunjik and Eslami, S.M. Ali and Rezende, Danilo and Rosenbaum, Dan},
	title = {From data to functa: Your data point is a function and you can treat it like one},
    booktitle = {International Conference on Machine Learning (ICML)},
	year = {2022},
	journal = {Proceedings of Machine Learning Research},
	volume = {162},
	pages = {5694 – 5725},
    doi={10.48550/arXiv.2201.12204}
}

@InProceedings{Grossbroemer2025Shape,
author="Gro{\ss}br{\"o}hmer, Christoph
and Falta, Fenja
and Keuth, Ron
and Kepp, Timo
and Heinrich, Mattias P.",
editor="Palm, Christoph
and Breininger, Katharina
and Deserno, Thomas
and Handels, Heinz
and Maier, Andreas
and Maier-Hein, Klaus H.
and Tolxdorff, Thomas M.",
title="Robust Statistical Shape Modelling with Implicit Neural Representations",
booktitle="Bildverarbeitung f{\"u}r die Medizin 2025",
year="2025",
publisher="Springer Fachmedien Wiesbaden",
address="Wiesbaden",
pages="32--37",
isbn="978-3-658-47422-5",
doi="10.1007/978-3-658-47422-5_10"
}

@inproceedings{dannecker_cina_2024,
	title = {{CINA}: {Conditional} {Implicit} {Neural} {Atlas} for {Spatio}-{Temporal} {Representation} of {Fetal} {Brains}},
	shorttitle = {{CINA}},
	author = {Dannecker, Maik and Kyriakopoulou, Vanessa and Cordero-Grande, Lucilio and Price, Anthony N. and Hajnal, Joseph V. and Rueckert, Daniel},
	year = {2024},
    booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2024},
    address="Cham",
    pages="181--191",
    abstract="We introduce a conditional implicit neural atlas (CINA) for spatio-temporal atlas generation from Magnetic Resonance Images (MRI) of the neurotypical and pathological fetal brain, that is fully independent of affine or non-rigid registration. During training, CINA learns a general representation of the fetal brain and encodes subject specific information into latent code. After training, CINA can construct a faithful atlas with tissue probability maps of the fetal brain for any gestational age (GA) and anatomical variation covered within the training domain. Thus, CINA is competent to represent both, neurotypical and pathological brains. Furthermore, a trained CINA model can be fit to brain MRI of unseen subjects via test-time optimization of the latent code. CINA can then produce probabilistic tissue maps tailored to a particular subject. We evaluate our method on a total of 198 T2 weighted MRI of normal and abnormal fetal brains from the dHCP and FeTA datasets. We demonstrate CINA's capability to represent a fetal brain atlas that can be flexibly conditioned on GA and on anatomical variations like ventricular volume or degree of cortical folding, making it a suitable tool for modeling both neurotypical and pathological brains. We quantify the fidelity of our atlas by means of tissue segmentation and age prediction and compare it to an established baseline. CINA demonstrates superior accuracy for neurotypical brains and pathological brains with ventriculomegaly. Moreover, CINA scores a mean absolute error of 0.23 weeks in fetal brain age prediction, further confirming an accurate representation of fetal brain development.",
    isbn="978-3-031-72114-4",
    doi="10.1007/978-3-031-72114-4_18"
}

@InProceedings{olesen_slicingBias_2025,
author="Olesen, Vincent
and Weng, Nina
and Feragen, Aasa
and Petersen, Eike",
editor="Puyol-Ant{\'o}n, Esther
and Zamzmi, Ghada
and Feragen, Aasa
and King, Andrew P.
and Cheplygina, Veronika
and Ganz-Benjaminsen, Melanie
and Ferrante, Enzo
and Glocker, Ben
and Petersen, Eike
and Baxter, John S. H.
and Rekik, Islem
and Eagleson, Roy",
title="Slicing Through Bias: Explaining Performance Gaps in Medical Image Analysis Using Slice Discovery Methods",
booktitle="Ethics and Fairness in Medical Imaging",
year="2025",
publisher="Springer Nature Switzerland",
address="Cham",
pages="3--13",
doi="10.1007/978-3-031-72787-0_1"
}

@InProceedings{wolterink_defImageReg_2022,
  title = 	 {Implicit Neural Representations for Deformable Image Registration},
  author =       {Wolterink, Jelmer M and Zwienenberg, Jesse C and Brune, Christoph},
  booktitle = 	 {Proceedings of The 5th International Conference on Medical Imaging with Deep Learning},
  pages = 	 {1349--1359},
  year = 	 {2022},
  editor = 	 {Konukoglu, Ender and Menze, Bjoern and Venkataraman, Archana and Baumgartner, Christian and Dou, Qi and Albarqouni, Shadi},
  volume = 	 {172},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {06--08 Jul},
  publisher =    {PMLR},
  abstract = 	 {Deformable medical image registration has in past years been revolutionized by the use of convolutional neural networks. These methods surpass conventional image registration techniques in speed but not in accuracy. Here, we present an alternative approach to leveraging neural networks for image registration. Instead of using a convolutional neural network to predict the transformation between images, we optimize a multi-layer perceptron to represent this transformation function. Using recent insights from differentiable rendering, we show how such an implicit deformable image registration (IDIR) model can be naturally combined with regularization terms based on standard automatic differentiation techniques. We demonstrate the effectiveness of this model on 4D chest CT registration in the DIR-LAB data set and find that a three-layer multi-layer perceptron with periodic activation functions outperforms all published deep learning-based results on this problem, without any folding and without the need for training data. The model is implemented using standard deep learning libraries and flexible enough to be extended to include different losses, regularizers, and optimization schemes.}
}

@inproceedings{stanley_flexible_2023,
    address = {Cham},
    title = {A {Flexible} {Framework} for {Simulating} and {Evaluating} {Biases} in {Deep} {Learning}-{Based} {Medical} {Image} {Analysis}},
    isbn = {978-3-031-43895-0},
    doi = {10.1007/978-3-031-43895-0_46},
    abstract = {Despite the remarkable advances in deep learning for medical image analysis, it has become evident that biases in datasets used for training such models pose considerable challenges for a clinical deployment, including fairness and domain generalization issues. Although the development of bias mitigation techniques has become ubiquitous, the nature of inherent and unknown biases in real-world medical image data prevents a comprehensive understanding of algorithmic bias when developing deep learning models and bias mitigation methods. To address this challenge, we propose a modular and customizable framework for bias simulation in synthetic but realistic medical imaging data. Our framework provides complete control and flexibility for simulating a range of bias scenarios that can lead to undesired model performance and shortcut learning. In this work, we demonstrate how this framework can be used to simulate morphological biases in neuroimaging data for disease classification with a convolutional neural network as a first feasibility analysis. Using this case example, we show how the proportion of bias in the disease class and proximity between disease and bias regions can affect model performance and explainability results. The proposed framework provides the opportunity to objectively and comprehensively study how biases in medical image data affect deep learning pipelines, which will facilitate a better understanding of how to responsibly develop models and bias mitigation methods for clinical use. Code is available at github.com/estanley16/SimBA.},
    language = {en},
    booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2023},
    publisher = {Springer Nature Switzerland},
    author = {Stanley, Emma A. M. and Wilms, Matthias and Forkert, Nils D.},
    editor = {Greenspan, Hayit and Madabhushi, Anant and Mousavi, Parvin and Salcudean, Septimiu and Duncan, James and Syeda-Mahmood, Tanveer and Taylor, Russell},
    year = {2023},
    pages = {489--499},
}

@article{kahrs_inr_2026,
    title = "Don’t Mind the Gaps: Implicit Neural Representations for Resolution-Agnostic Retinal OCT Analysis",
    author = "Kahrs, Bennet and Andresen, Julia and Falta, Fenja and Santarossa, Monty and Handels, Heinz and Kepp, Timo",
    journal = "Machine Learning for Biomedical Imaging",
    volume = "2026",
    issue = "MELBA–BVM 2025 Special Issue",
    year = "2026",
    pages = "59--77",
    issn = "2766-905X",
    doi = "10.59275/j.melba.2026-38ba",
}

@article{rohlfing_sri24_2009,
    title = {The {SRI24} multichannel atlas of normal adult human brain structure},
    volume = {31},
    issn = {1065-9471},
    doi = {10.1002/hbm.20906},
    abstract = {This article describes the SRI24 atlas, a new standard reference system of normal human brain anatomy, that was created using template‐free population registration of high‐resolution magnetic resonance images acquired at 3T in a group of 24 normal control subjects. The atlas comprises anatomical channels (T1, T2, and proton density weighted), diffusion‐related channels (fractional anisotropy, mean diffusivity, longitudinal diffusivity, mean diffusion‐weighted image), tissue channels (CSF probability, gray matter probability, white matter probability, tissue labels), and two cortical parcellation maps. The SRI24 atlas enables multichannel atlas‐to‐subject image registration. It is uniquely versatile in that it is equally suited for the two fundamentally different atlas applications: label propagation and spatial normalization. Label propagation, herein demonstrated using diffusion tensor image fiber tracking, is enabled by the increased sharpness of the SRI24 atlas compared with other available atlases. Spatial normalization, herein demonstrated using data from a young–old group comparison study, is enabled by its unbiased average population shape property. For both propagation and normalization, we also report the results of quantitative comparisons with seven other published atlases: Colin27, MNI152, ICBM452 (warp5 and air12), and LPBA40 (SPM5, FLIRT, AIR). Our results suggest that the SRI24 atlas, although based on 3T MR data, allows equally accurate spatial normalization of data acquired at 1.5T as the comparison atlases, all of which are based on 1.5T data. Furthermore, the SRI24 atlas is as suitable for label propagation as the comparison atlases and detailed enough to allow delineation of anatomical structures for this purpose directly in the atlas. Hum Brain Mapp, 2010. © 2009 Wiley‐Liss, Inc.},
    number = {5},
    journal = {Human Brain Mapping},
    author = {Rohlfing, Torsten and Zahr, Natalie M. and Sullivan, Edith V. and Pfefferbaum, Adolf},
    month = dec,
    year = {2009},
    pmid = {20017133},
    pmcid = {PMC2915788},
    pages = {798--819},
}

@inproceedings{eyuboglu_domino_2022,
    title = {Domino: {Discovering} {Systematic} {Errors} with {Cross}-{Modal} {Embeddings}},
    shorttitle = {Domino},
    abstract = {Machine learning models that achieve high overall accuracy often make systematic errors on important subsets (or slices) of data. Identifying underperforming slices is particularly challenging when working with high-dimensional inputs (e.g. images, audio), where important slices are often unlabeled. In order to address this issue, recent studies have proposed automated slice discovery methods (SDMs), which leverage learned model representations to mine input data for slices on which a model performs poorly. To be useful to a practitioner, these methods must identify slices that are both underperforming and coherent (i.e. united by a human-understandable concept). However, no quantitative evaluation framework currently exists for rigorously assessing SDMs with respect to these criteria. Additionally, prior qualitative evaluations have shown that SDMs often identify slices that are incoherent. In this work, we address these challenges by first designing a principled evaluation framework that enables a quantitative comparison of SDMs across 1,235 slice discovery settings in three input domains (natural images, medical images, and time-series data). Then, motivated by the recent development of powerful cross-modal representation learning approaches, we present Domino, an SDM that leverages cross-modal embeddings and a novel error-aware mixture model to discover and describe coherent slices. We find that Domino accurately identifies 36\% of the 1,235 slices in our framework - a 12 percentage point improvement over prior methods. Further, Domino is the first SDM that can provide natural language descriptions of identified slices, correctly generating the exact name of the slice in 35\% of settings.},
    language = {en},
    author = {Eyuboglu, Sabri and Varma, Maya and Saab, Khaled and Delbrouck, Jean-Benoit and Lee-Messer, Christopher and Dunnmon, Jared and Zou, James and Ré, Christopher},
    year = {2022},
    booktitle = {International Conference on Learning Representations (ICLR)},
    doi= {10.48550/arXiv.2203.14960}
}


@inproceedings{bissoto_subgroup_2025,
    author = {Bissoto, Alceu and Hoang, Trung-Dung and Flühmann, Tim and Sun, Susu and Baumgartner, Christian F and Koch, Lisa M},
    title = {Subgroup {Performance} {Analysis} in {Hidden} {Stratifications}},
    booktitle = {Medical Image Computing and Computer Assisted Intervention – MICCAI 2025},
    year = {2025},
    publisher="Springer Nature Switzerland",
    address="Cham",
    pages="594--603",
    isbn="978-3-032-05185-1",
    doi="10.1007/978-3-032-05185-1_57"
}

@article{chawla_smote_2002,
    title = {{SMOTE}: {Synthetic} {Minority} {Over}-sampling {Technique}},
    volume = {16},
    issn = {1076-9757},
    shorttitle = {{SMOTE}},
    doi = {10.1613/jair.953},
    abstract = {An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of "normal" examples with only a small percentage of "abnormal" or "interesting" examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.},
    journal = {Journal of Artificial Intelligence Research},
    author = {Chawla, Nitesh and Bowyer, Kevin and Hall, L. and Kegelmeyer, W.},
    month = jun,
    year = {2002},
    keywords = {Computer Science - Artificial Intelligence},
    pages = {321--357},
}

@article{zhang_multimodal_2025,
    title = {A {Multimodal} {Biomedical} {Foundation} {Model} {Trained} from {Fifteen} {Million} {Image}–{Text} {Pairs}},
    volume = {2},
    doi = {10.1056/AIoa2400640},
    number = {1},
    journal = {NEJM AI},
    author = {Zhang, Sheng and Xu, Yanbo and Usuyama, Naoto and Xu, Hanwen and Bagga, Jaspreet and Tinn, Robert and Preston, Sam and Rao, Rajesh and Wei, Mu and Valluri, Naveen and Wong, Cliff and Tupini, Andrea and Wang, Yu and Mazzola, Matt and Shukla, Swadheen and Liden, Lars and Gao, Jianfeng and Crabtree, Angela and Piening, Brian and Bifulco, Carlo and Lungren, Matthew P. and Naumann, Tristan and Wang, Sheng and Poon, Hoifung},
    year = {2025},
    note = {Publisher: Massachusetts Medical Society},
    pages = {AIoa2400640},
}

@article{plumb2023towards,
title={Towards a More Rigorous Science of Blindspot Discovery in Image Classification Models},
author={Gregory Plumb and Nari Johnson and Angel Cabrera and Ameet Talwalkar},
journal={Transactions on Machine Learning Research (TMLR)},
issn={2835-8856},
year={2023},
doi={10.48550/arXiv.2207.04104}
}

@article{radford_learning_2021,
    title = {Learning {Transferable} {Visual} {Models} {From} {Natural} {Language} {Supervision}},
    abstract = {SOTA computer vision systems are trained to predict a ﬁxed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efﬁcient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study performance on over 30 different computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of ﬁne-grained object classiﬁcation. The model transfers nontrivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset speciﬁc training. For instance, we match the accuracy of the original ResNet50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP.},
    journal = {International Conference on Machine Learning (ICLR)},
    author = {Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and Krueger, Gretchen and Sutskever, Ilya},
    year = {2021},
    doi={10.48550/arXiv.2103.00020}
}

@article{seyyed-kalantari_underdiagnosis_2021,
    title = {Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations},
    volume = {27},
    copyright = {2021 The Author(s)},
    issn = {1546-170X},
    doi = {10.1038/s41591-021-01595-0},
    abstract = {Artificial intelligence (AI) systems have increasingly achieved expert-level performance in medical imaging applications. However, there is growing concern that such AI systems may reflect and amplify human bias, and reduce the quality of their performance in historically under-served populations such as female patients, Black patients, or patients of low socioeconomic status. Such biases are especially troubling in the context of underdiagnosis, whereby the AI algorithm would inaccurately label an individual with a disease as healthy, potentially delaying access to care. Here, we examine algorithmic underdiagnosis in chest X-ray pathology classification across three large chest X-ray datasets, as well as one multi-source dataset. We find that classifiers produced using state-of-the-art computer vision techniques consistently and selectively underdiagnosed under-served patient populations and that the underdiagnosis rate was higher for intersectional under-served subpopulations, for example, Hispanic female patients. Deployment of AI systems using medical imaging for disease diagnosis with such biases risks exacerbation of existing care biases and can potentially lead to unequal access to medical treatment, thereby raising ethical concerns for the use of these models in the clinic.},
    language = {en},
    number = {12},
    journal = {Nature Medicine},
    author = {Seyyed-Kalantari, Laleh and Zhang, Haoran and McDermott, Matthew B. A. and Chen, Irene Y. and Ghassemi, Marzyeh},
    month = dec,
    year = {2021},
    note = {Publisher: Nature Publishing Group},
    keywords = {Machine learning, Medical imaging},
    pages = {2176--2182},
}

@inproceedings{larrazabal_anatomical_2019,
    title = {Anatomical {Priors} for {Image} {Segmentation} via {Post}-{Processing} with {Denoising} {Autoencoders}},
    abstract = {Deep convolutional neural networks (CNN) proved to be highly accurate to perform anatomical segmentation of medical images. However, some of the most popular CNN architectures for image segmentation still rely on post-processing strategies (e.g. Conditional Random Fields) to incorporate connectivity constraints into the resulting masks. These post-processing steps are based on the assumption that objects are usually continuous and therefore nearby pixels should be assigned the same object label. Even if it is a valid assumption in general, these methods do not offer a straightforward way to incorporate more complex priors like convexity or arbitrary shape restrictions. In this work we propose Post-DAE, a post-processing method based on denoising autoencoders (DAE) trained using only segmentation masks. We learn a low-dimensional space of anatomically plausible segmentations, and use it as a post-processing step to impose shape constraints on the resulting masks obtained with arbitrary segmentation methods. Our approach is independent of image modality and intensity information since it employs only segmentation masks for training. This enables the use of anatomical segmentations that do not need to be paired with intensity images, making the approach very flexible. Our experimental results on anatomical segmentation of X-ray images show that Post-DAE can improve the quality of noisy and incorrect segmentation masks obtained with a variety of standard methods, by bringing them back to a feasible space, with almost no extra computational time.},
    author = {Larrazabal, Agostina J. and Martinez, Cesar and Ferrante, Enzo},
    year = {2019},
    booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2019},
    publisher={Springer International Publishing},
    address={Cham},
    pages={585--593},
    isbn={978-3-030-32226-7},
    doi={10.1007/978-3-030-32226-7_65}
}

@article{brown_detecting_2023,
    title = {Detecting shortcut learning for fair medical {AI} using shortcut testing},
    volume = {14},
    copyright = {2023 The Author(s)},
    issn = {2041-1723},
    doi = {10.1038/s41467-023-39902-7},
    abstract = {Machine learning (ML) holds great promise for improving healthcare, but it is critical to ensure that its use will not propagate or amplify health disparities. An important step is to characterize the (un)fairness of ML models—their tendency to perform differently across subgroups of the population—and to understand its underlying mechanisms. One potential driver of algorithmic unfairness, shortcut learning, arises when ML models base predictions on improper correlations in the training data. Diagnosing this phenomenon is difficult as sensitive attributes may be causally linked with disease. Using multitask learning, we propose a method to directly test for the presence of shortcut learning in clinical ML systems and demonstrate its application to clinical tasks in radiology and dermatology. Finally, our approach reveals instances when shortcutting is not responsible for unfairness, highlighting the need for a holistic approach to fairness mitigation in medical AI.},
    language = {en},
    number = {1},
    journal = {Nature Communications},
    author = {Brown, Alexander and Tomasev, Nenad and Freyberg, Jan and Liu, Yuan and Karthikesalingam, Alan and Schrouff, Jessica},
    month = jul,
    year = {2023},
    note = {Publisher: Nature Publishing Group},
    keywords = {Computer science, Machine learning, Medical imaging},
    pages = {4314},
}
@article{stanley_towards_2024,
    title = {Towards objective and systematic evaluation of bias in artificial intelligence for medical imaging},
    volume = {31},
    issn = {1527-974X},
    doi = {10.1093/jamia/ocae165},
    abstract = {Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit bias in the form of subgroup performance disparities. However, since not all sources of bias in real-world medical imaging data are easily identifiable, it is challenging to comprehensively assess their impacts. In this article, we introduce an analysis framework for systematically and objectively investigating the impact of biases in medical images on AI models.Our framework utilizes synthetic neuroimages with known disease effects and sources of bias. We evaluated the impact of bias effects and the efficacy of 3 bias mitigation strategies in counterfactual data scenarios on a convolutional neural network (CNN) classifier.The analysis revealed that training a CNN model on the datasets containing bias effects resulted in expected subgroup performance disparities. Moreover, reweighing was the most successful bias mitigation strategy for this setup. Finally, we demonstrated that explainable AI methods can aid in investigating the manifestation of bias in the model using this framework.The value of this framework is showcased in our findings on the impact of bias scenarios and efficacy of bias mitigation in a deep learning model pipeline. This systematic analysis can be easily expanded to conduct further controlled in silico trials in other investigations of bias in medical imaging AI.Our novel methodology for objectively studying bias in medical imaging AI can help support the development of clinical decision-support tools that are robust and responsible.},
    number = {11},
    journal = {Journal of the American Medical Informatics Association},
    author = {Stanley, Emma A M and Souza, Raissa and Winder, Anthony J and Gulve, Vedant and Amador, Kimberly and Wilms, Matthias and Forkert, Nils D},
    month = nov,
    year = {2024},
    pages = {2613--2621},
}
@article{glocker_algorithmic_2023,
    title = {Algorithmic encoding of protected characteristics in chest {X}-ray disease detection models},
    volume = {89},
    issn = {2352-3964},
    doi = {10.1016/j.ebiom.2023.104467},
    language = {English},
    journal = {eBioMedicine},
    author = {Glocker, Ben and Jones, Charles and Bernhardt, Mélanie and Winzeck, Stefan},
    month = mar,
    year = {2023},
    pmid = {36791660},
    note = {Publisher: Elsevier},
    keywords = {Algorithmic bias, Artificial intelligence, Image-based disease detection, Subgroup disparities},
}
@article{stanley_where_2025,
    title = {Where, why, and how is bias learned in medical image analysis models? {A} study of bias encoding within convolutional networks using synthetic data},
    volume = {111},
    issn = {2352-3964},
    shorttitle = {Where, why, and how is bias learned in medical image analysis models?},
    doi = {10.1016/j.ebiom.2024.105501},
    language = {English},
    journal = {eBioMedicine},
    author = {Stanley, Emma A. M. and Souza, Raissa and Wilms, Matthias and Forkert, Nils D.},
    month = jan,
    year = {2025},
    pmid = {39671751},
    note = {Publisher: Elsevier},
    keywords = {Algorithmic bias, Artificial intelligence, Synthetic data},
}
@article{raz_regional_2005,
    title = {Regional brain changes in aging healthy adults: general trends, individual differences and modifiers},
    volume = {15},
    issn = {1047-3211},
    shorttitle = {Regional brain changes in aging healthy adults},
    doi = {10.1093/cercor/bhi044},
    abstract = {Brain aging research relies mostly on cross-sectional studies, which infer true changes from age differences. We present longitudinal measures of five-year change in the regional brain volumes in healthy adults. Average and individual differences in volume changes and the effects of age, sex and hypertension were assessed with latent difference score modeling. The caudate, the cerebellum, the hippocampus and the association cortices shrunk substantially. There was minimal change in the entorhinal and none in the primary visual cortex. Longitudinal measures of shrinkage exceeded cross-sectional estimates. All regions except the inferior parietal lobule showed individual differences in change. Shrinkage of the cerebellum decreased from young to middle adulthood, and increased from middle adulthood to old age. Shrinkage of the hippocampus, the entorhinal cortices, the inferior temporal cortex and the prefrontal white matter increased with age. Moreover, shrinkage in the hippocampus and the cerebellum accelerated with age. In the hippocampus, both linear and quadratic trends in incremental age-related shrinkage were limited to the hypertensive participants. Individual differences in shrinkage correlated across some regions, suggesting common causes. No sex differences in age trends except for the caudate were observed. We found no evidence of neuroprotective effects of larger brain size or educational attainment.},
    language = {eng},
    number = {11},
    journal = {Cerebral Cortex (New York, N.Y.: 1991)},
    author = {Raz, Naftali and Lindenberger, Ulman and Rodrigue, Karen M. and Kennedy, Kristen M. and Head, Denise and Williamson, Adrienne and Dahle, Cheryl and Gerstorf, Denis and Acker, James D.},
    month = nov,
    year = {2005},
    pmid = {15703252},
    keywords = {Adult, Age Factors, Aged, Aged, 80 and over, Aging, Body Constitution, Brain, Female, Humans, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Magnetic Resonance Imaging, Male, Middle Aged, Organ Size, Reproducibility of Results, Sensitivity and Specificity, Sex Factors},
    pages = {1676--1689},
}
@article{ruigrok_meta-analysis_2014,
    title = {A meta-analysis of sex differences in human brain structure},
    volume = {39},
    issn = {1873-7528},
    doi = {10.1016/j.neubiorev.2013.12.004},
    abstract = {The prevalence, age of onset, and symptomatology of many neuropsychiatric conditions differ between males and females. To understand the causes and consequences of sex differences it is important to establish where they occur in the human brain. We report the first meta-analysis of typical sex differences on global brain volume, a descriptive account of the breakdown of studies of each compartmental volume by six age categories, and whole-brain voxel-wise meta-analyses on brain volume and density. Gaussian-process regression coordinate-based meta-analysis was used to examine sex differences in voxel-based regional volume and density. On average, males have larger total brain volumes than females. Examination of the breakdown of studies providing total volumes by age categories indicated a bias towards the 18-59 year-old category. Regional sex differences in volume and tissue density include the amygdala, hippocampus and insula, areas known to be implicated in sex-biased neuropsychiatric conditions. Together, these results suggest candidate regions for investigating the asymmetric effect that sex has on the developing brain, and for understanding sex-biased neurological and psychiatric conditions.},
    language = {eng},
    number = {100},
    journal = {Neuroscience and Biobehavioral Reviews},
    author = {Ruigrok, Amber N. V. and Salimi-Khorshidi, Gholamreza and Lai, Meng-Chuan and Baron-Cohen, Simon and Lombardo, Michael V. and Tait, Roger J. and Suckling, John},
    month = feb,
    year = {2014},
    pmid = {24374381},
    pmcid = {PMC3969295},
    keywords = {Adolescent, Adult, Aged, Aged, 80 and over, Brain, Child, Child, Preschool, Female, Gaussian-process regression (GPR), Humans, Male, Meta-analysis, Middle Aged, Sex Factors, Sex differences, Volume, Voxel-based morphometry, Young Adult},
    pages = {34--50},
}
@inproceedings{sun_right_2023,
    address = {Cham},
    title = {Right for the {Wrong} {Reason}: {Can} {Interpretable} {ML} {Techniques} {Detect} {Spurious} {Correlations}?},
    isbn = {978-3-031-43895-0},
    shorttitle = {Right for the {Wrong} {Reason}},
    doi = {10.1007/978-3-031-43895-0_40},
    abstract = {While deep neural network models offer unmatched classification performance, they are prone to learning spurious correlations in the data. Such dependencies on confounding information can be difficult to detect using performance metrics if the test data comes from the same distribution as the training data. Interpretable ML methods such as post-hoc explanations or inherently interpretable classifiers promise to identify faulty model reasoning. However, there is mixed evidence whether many of these techniques are actually able to do so. In this paper, we propose a rigorous evaluation strategy to assess an explanation technique’s ability to correctly identify spurious correlations. Using this strategy, we evaluate five post-hoc explanation techniques and one inherently interpretable method for their ability to detect three types of artificially added confounders in a chest x-ray diagnosis task. We find that the post-hoc technique SHAP, as well as the inherently interpretable Attri-Net provide the best performance and can be used to reliably identify faulty model behavior.},
    language = {en},
    booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2023},
    publisher = {Springer Nature Switzerland},
    author = {Sun, Susu and Koch, Lisa M. and Baumgartner, Christian F.},
    editor = {Greenspan, Hayit and Madabhushi, Anant and Mousavi, Parvin and Salcudean, Septimiu and Duncan, James and Syeda-Mahmood, Tanveer and Taylor, Russell},
    year = {2023},
    keywords = {Confounder detection, Interpretable machine learning},
    pages = {425--434},
}
@article{selvaraju_grad-cam_2017,
  title={{Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization}},
  author={Ramprasaath R. Selvaraju and Abhishek Das and Ramakrishna Vedantam and Michael Cogswell and Devi Parikh and Dhruv Batra},
  journal={International Journal of Computer Vision},
  year={2016},
  volume={128},
  pages={336 - 359},
  doi="10.1007/s11263-019-01228-7"
}

@inproceedings{zhang_improving_2022,
  author       = {Haoran Zhang and
                  Natalie Dullerud and
                  Karsten Roth and
                  Lauren Oakden{-}Rayner and
                  Stephen Pfohl and
                  Marzyeh Ghassemi},
  title        = {Improving the Fairness of Chest X-ray Classifiers},
  booktitle    = {Conference on Health, Inference, and Learning, {CHIL} 2022, 7-8 April
                  2022, Virtual Event},
  series       = {Proceedings of Machine Learning Research},
  pages        = {204--233},
  publisher    = {{PMLR}},
  year         = {2022},
  biburl       = {https://dblp.org/rec/conf/chil/ZhangDROPG22.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org},
  doi="10.48550/arXiv.2203.12609"
}

@inproceedings{zietlow_leveling_2022,
    address = {New Orleans, LA, USA},
    title = {Leveling {Down} in {Computer} {Vision}: {Pareto} {Inefficiencies} in {Fair} {Deep} {Classifiers}},
    copyright = {https://doi.org/10.15223/policy-029},
    isbn = {978-1-6654-6946-3},
    shorttitle = {Leveling {Down} in {Computer} {Vision}},
    doi = {10.1109/CVPR52688.2022.01016},
    abstract = {Algorithmic fairness is frequently motivated in terms of a trade-off in which overall performance is decreased so as to improve performance on disadvantaged groups where the algorithm would otherwise be less accurate. Contrary to this, we find that applying existing fairness approaches to computer vision improve fairness by degrading the performance of classifiers across all groups (with increased degradation on the best performing groups).},
    language = {en},
    booktitle = {2022 {IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})},
    publisher = {IEEE},
    author = {Zietlow, Dominik and Lohaus, Michael and Balakrishnan, Guha and Kleindessner, Matthaus and Locatello, Francesco and Scholkopf, Bernhard and Russell, Chris},
    year = {2022},
    pages = {10400--10411},
}