Addressing fairness issues in deep learning-based medical image analysis: a systematic review
Deep learning algorithms have demonstrated remarkable efficacy in various medical image
analysis (MedIA) applications. However, recent research highlights a performance disparity …
analysis (MedIA) applications. However, recent research highlights a performance disparity …
Exploring transformer reliability in clinically significant prostate cancer segmentation: A comprehensive in-depth investigation
Despite the growing prominence of transformers in medical image segmentation, their
application to clinically significant prostate cancer (csPCa) has been overlooked. Minimal …
application to clinically significant prostate cancer (csPCa) has been overlooked. Minimal …
Understanding Disparities in Post Hoc Machine Learning Explanation
Previous work has highlighted that existing post-hoc explanation methods exhibit disparities
in explanation fidelity (across “race” and “gender” as sensitive attributes), and while a large …
in explanation fidelity (across “race” and “gender” as sensitive attributes), and while a large …
Drop the shortcuts: image augmentation improves fairness and decreases AI detection of race and other demographics from medical images
Background It has been shown that AI models can learn race on medical images, leading to
algorithmic bias. Our aim in this study was to enhance the fairness of medical image models …
algorithmic bias. Our aim in this study was to enhance the fairness of medical image models …
Slicing Through Bias: Explaining Performance Gaps in Medical Image Analysis Using Slice Discovery Methods
Abstract Machine learning models have achieved high overall accuracy in medical image
analysis. However, performance disparities on specific patient groups pose challenges to …
analysis. However, performance disparities on specific patient groups pose challenges to …
Uncertainty in latent representations of variational autoencoders optimized for visual tasks
Deep learning methods are increasingly becoming instrumental as modeling tools in
computational neuroscience, employing optimality principles to build bridges between …
computational neuroscience, employing optimality principles to build bridges between …
Deep learning methods for localization, segmentation and robustness in medical imaging
O Laousy - 2024 - theses.hal.science
In recent years, there has been a remarkable surge in advancements at the crossroads of
deep learning and medicine, particularly in the realm of medical imaging. This rapid …
deep learning and medicine, particularly in the realm of medical imaging. This rapid …
A Study of Age and Sex Bias in Multiple Instance Learning Based Classification of Acute Myeloid Leukemia Subtypes
Abstract Accurate classification of Acute Myeloid Leukemia (AML) subtypes is crucial for
clinical decision-making and patient care. In this study, we investigate the potential presence …
clinical decision-making and patient care. In this study, we investigate the potential presence …