44 citations · 57 across the 7 of their papers we have counts for
9 papers
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-…
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…
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-…
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…
Non-parametric Uni-modality Constraints for Deep Ordinal Classification
Soufiane Belharbi, Ismail Ben Ayed, Luke McCaffrey +1
We propose a new constrained-optimization formulation for deep ordinal classification, in which uni-modality of the label distribution is enforced implicitly via a set of inequalit…
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…
Neural Networks Regularization Through Representation Learning
Soufiane Belharbi
Neural network models and deep models are one of the leading and state of the art models in machine learning. Most successful deep neural models are the ones with many layers which…
Neural Networks Regularization Through Class-wise Invariant Representation Learning
Soufiane Belharbi, Clément Chatelain, Romain Hérault +1
Training deep neural networks is known to require a large number of training samples. However, in many applications only few training samples are available. In this work, we tackle…
Deep Neural Networks Regularization for Structured Output Prediction
Soufiane Belharbi, Romain Hérault, Clément Chatelain +1
A deep neural network model is a powerful framework for learning representations. Usually, it is used to learn the relation by exploiting the regularities in the input $x…