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Exploring Entropy-based Active Learning for Fair Brain Segmentation
Ghazal Danaee, Mélanie Gaillochet, Christian Desrosiers +2
Active learning (AL) has emerged as a crucial strategy for reducing the prohibitive costs associated with medical image segmentation. However, standard uncertainty-based AL methods…
Anatomically-aware conformal prediction for medical image segmentation with random walks
Mélanie Gaillochet, Christian Desrosiers, Hervé Lombaert
The reliable deployment of deep learning in medical imaging requires uncertainty quantification that provides rigorous error guarantees while remaining anatomically meaningful. Con…
Prompt learning with bounding box constraints for medical image segmentation
Mélanie Gaillochet, Mehrdad Noori, Sahar Dastani +2
Pixel-wise annotations are notoriously labourious and costly to obtain in the medical domain. To mitigate this burden, weakly supervised approaches based on bounding box annotation…
Automating MedSAM by Learning Prompts with Weak Few-Shot Supervision
Mélanie Gaillochet, Christian Desrosiers, Hervé Lombaert
Foundation models such as the recently introduced Segment Anything Model (SAM) have achieved remarkable results in image segmentation tasks. However, these models typically require…
TAAL: Test-time Augmentation for Active Learning in Medical Image Segmentation
Mélanie Gaillochet, Christian Desrosiers, Hervé Lombaert
Deep learning methods typically depend on the availability of labeled data, which is expensive and time-consuming to obtain. Active learning addresses such effort by prioritizing w…
Active learning for medical image segmentation with stochastic batches
Mélanie Gaillochet, Christian Desrosiers, Hervé Lombaert
The performance of learning-based algorithms improves with the amount of labelled data used for training. Yet, manually annotating data is particularly difficult for medical image…