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20152026
most citedDeep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey

44 citations · 57 across the 7 of their papers we have counts for

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cs.CV2026

Test Time Adaptation Methods for Point Cloud Registration in Laparoscopic Surgery

Nina Bodelot, Soufiane Belharbi, Eric Granger

3D point cloud registration in laparoscopic surgery estimates the transformation between an intraoperative organ reconstructed from video and its preoperative mesh. Because ground-…

cs.CV2020

Deep Active Learning for Joint Classification & Segmentation with Weak Annotator

Soufiane Belharbi, Ismail Ben Ayed, Luke McCaffrey +1

CNN visualization and interpretation methods, like class-activation maps (CAMs), are typically used to highlight the image regions linked to class predictions. These models allow t…

cs.CV2020

Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images via Max-Min Uncertainty

Soufiane Belharbi, Jérôme Rony, Jose Dolz +3

Weakly-supervised learning (WSL) has recently triggered substantial interest as it mitigates the lack of pixel-wise annotations. Given global image labels, WSL methods yield pixel-…

cs.CV2019

Convolutional STN for Weakly Supervised Object Localization

Akhil Meethal, Marco Pedersoli, Soufiane Belharbi +1

Weakly supervised object localization is a challenging task in which the object of interest should be localized while learning its appearance. State-of-the-art methods recycle the…

cs.CV2019★ 44 cited

Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey

Jérôme Rony, Soufiane Belharbi, Jose Dolz +3

Using deep learning models to diagnose cancer from histology data presents several challenges. Cancer grading and localization of regions of interest (ROIs) in these images normall…