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Found 8 papers, 1 papers with code
Fast Diffusion-Based Counterfactuals for Shortcut Removal and Generation
Via a novel inpainting-based modification we spatially limit the changes made with no extra inference step, encouraging the removal of spatially constrained shortcut features while ensuring that the shortcut-free counterfactuals preserve their remaining image features to a high degree.
Are Sex-based Physiological Differences the Cause of Gender Bias for Chest X-ray Diagnosis?
In this work, we investigate the causes of gender bias in machine learning-based chest X-ray diagnosis.
Are demographically invariant models and representations in medical imaging fair?
Medical imaging models have been shown to encode information about patient demographics such as age, race, and sex in their latent representation, raising concerns about their potential for discrimination.
That Label's Got Style: Handling Label Style Bias for Uncertain Image Segmentation
Segmentation uncertainty models predict a distribution over plausible segmentations for a given input, which they learn from the annotator variation in the training set.
On (assessing) the fairness of risk score models
Further, we address how to assess the fairness of risk score models quantitatively, including a discussion of metric choices and meaningful statistical comparisons between groups.
Feature robustness and sex differences in medical imaging: a case study in MRI-based Alzheimer's disease detection
Instead, while logistic regression is fully robust to dataset composition, we find that CNN performance is generally improved for both male and female subjects when including more female subjects in the training dataset.
Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions
This survey provides an overview of the technical and procedural challenges involved in creating medical machine learning systems responsibly and in conformity with existing regulations, as well as possible solutions to address these challenges.
On Approximate Nonlinear Gaussian Message Passing On Factor Graphs
Factor graphs have recently gained increasing attention as a unified framework for representing and constructing algorithms for signal processing, estimation, and control.
