5 papers
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…
Joint reconstruction and bias field correction for undersampled MR imaging
Mélanie Gaillochet, Kerem C. Tezcan, Ender Konukoglu
Undersampling the k-space in MRI allows saving precious acquisition time, yet results in an ill-posed inversion problem. Recently, many deep learning techniques have been developed…