Melanie Gaillochet

Exploring Entropy-based Active Learning for Fair Brain Segmentation

Ghazal Danaee

Christian Desrosiers

Sylvain Bouix

Active learning (AL) has emerged as a crucial strategy for reducing the prohibitive costs associated with medical image segmentation. Howeve… (see more)r, standard uncertainty-based AL methods typically focus on maximizing performance metrics, ignoring performance disparities or fairness across groups with sensitive attributes. While fair active learning has been explored in classification tasks, its intersection with medical image segmentation remains unaddressed. In this work, we introduced a fairness-aware active learning framework with a Weighted Entropy selection strategy that modulates uncertainty based on current group-specific performance estimates on the labeled set. To decouple true epistemic uncertainty from anatomical volume variances, we further utilized a masked, scaled entropy restricted to the region of interest. The framework was evaluated on synthetic T1-weighted brain MRIs with controlled left caudate bias in both strong and weak bias settings. A 3D U-Net was trained to segment the left caudate under several AL strategies, starting from both demographically balanced and strongly imbalanced initial labeled sets. Experiments demonstrated that our method markedly reduces performance disparities between groups compared to random sampling and standard uncertainty sampling. By prioritizing poorly segmented subgroups during the AL cycles, our method consistently achieved the highest equity-scaled performance and reduced the disparity metric by 75% (strong bias) and 86% (weak bias) relative to standard entropy at the final budget. Overall, this work is among the first studies on fair AL for medical image segmentation, offering an efficient strategy to train more equitable models in resource-constrained environments.

2026-05-04

Medical Imaging with Deep Learning (published)

doi.org

proceedings.mlr.press

Anatomically-aware conformal prediction for medical image segmentation with random walks

Melanie Gaillochet

Christian Desrosiers

Hervé Lombaert

2026-01-25

ArXiv (preprint)

arxiv.org

Prompt learning with bounding box constraints for medical image segmentation.

Melanie Gaillochet

Mehrdad Noori

Sahar Dastani

Christian Desrosiers

Hervé Lombaert

Pixel-wise annotations are notoriously labourious and costly to obtain in the medical domain. To mitigate this burden, weakly supervised app… (see more)roaches based on bounding box annotations-much easier to acquire-offer a practical alternative. Vision foundation models have recently shown noteworthy segmentation performance when provided with prompts such as points or bounding boxes. Prompt learning exploits these models by adapting them to downstream tasks and automating segmentation, thereby reducing user intervention. However, existing prompt learning approaches depend on fully annotated segmentation masks. This paper proposes a novel framework that combines the representational power of foundation models with the annotation efficiency of weakly supervised segmentation. More specifically, our approach automates prompt generation for foundation models using only bounding box annotations. Our proposed optimization scheme integrates multiple constraints derived from box annotations with pseudo-labels generated by the prompted foundation model. Extensive experiments across multi-modal datasets reveal that our weakly supervised method achieves an average Dice score of 84.90% in a limited data setting, outperforming existing fully-supervised and weakly-supervised approaches. The code will be available upon acceptance

2025-06-23

IEEE transactions on bio-medical engineering (published)

doi.org

arxiv.org

Automating MedSAM by Learning Prompts with Weak Few-Shot Supervision

Melanie Gaillochet

Christian Desrosiers

Hervé Lombaert

2024-09-27

Lecture Notes in Computer Science (published)

doi.org

arxiv.org