44 citations · 66 across the 16 of their papers we have counts for
16 papers
Selective Prediction Forgetting for Adaptation of Weakly Supervised Localization of Histology Images
Alexis Guichemerre, Banafsheh Karimian, Soufiane Belharbi +6
Under strong cross-domain shifts, weakly supervised object localization (WSOL) models often lose their capacity to discriminate and develop a strong prediction bias toward a subset…
Longitudinal Risk Prediction in Mammography with Privileged History Distillation
Banafsheh Karimian, Soufiane Belharbi, Alexis Guichemerre +3
Longitudinal mammography screening has become an important source of information for improving future breast cancer risk prediction. However, the performance of current longitudina…
CLIP-IT: CLIP-based Pairing for Histology Images Classification
Banafsheh Karimian, Giulia Avanzato, Soufian Belharbi +4
Multimodal learning has shown promise in medical imaging, combining complementary modalities like images and text. Vision-language models (VLMs) capture rich diagnostic cues but of…
PixelCAM: Pixel Class Activation Mapping for Histology Image Classification and ROI Localization
Alexis Guichemerre, Soufiane Belharbi, Mohammadhadi Shateri +2
Weakly supervised object localization (WSOL) methods allow training models to classify images and localize ROIs. WSOL only requires low-cost image-class annotations yet provides a…
SR-CACO-2: A Dataset for Confocal Fluorescence Microscopy Image Super-Resolution
Soufiane Belharbi, Mara KM Whitford, Phuong Hoang +3
Confocal fluorescence microscopy is one of the most accessible and widely used imaging techniques for the study of biological processes at the cellular and subcellular levels. Scan…
Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histology
Alexis Guichemerre, Soufiane Belharbi, Tsiry Mayet +4
Given the emergence of deep learning, digital pathology has gained popularity for cancer diagnosis based on histology images. Deep weakly supervised object localization (WSOL) mode…
CoLo-CAM: Class Activation Mapping for Object Co-Localization in Weakly-Labeled Unconstrained Videos
Soufiane Belharbi, Shakeeb Murtaza, Marco Pedersoli +3
Leveraging spatiotemporal information in videos is critical for weakly supervised video object localization (WSVOL) tasks. However, state-of-the-art methods only rely on visual and…
TCAM: Temporal Class Activation Maps for Object Localization in Weakly-Labeled Unconstrained Videos
Soufiane Belharbi, Ismail Ben Ayed, Luke McCaffrey +1
Weakly supervised video object localization (WSVOL) allows locating object in videos using only global video tags such as object class. State-of-art methods rely on multiple indepe…
Leveraging Uncertainty for Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images
Soufiane Belharbi, Jérôme Rony, Jose Dolz +3
Trained using only image class label, deep weakly supervised methods allow image classification and ROI segmentation for interpretability. Despite their success on natural images,…
Negative Evidence Matters in Interpretable Histology Image Classification
Soufiane Belharbi, Marco Pedersoli, Ismail Ben Ayed +2
Using only global image-class labels, weakly-supervised learning methods, such as class activation mapping, allow training CNNs to jointly classify an image, and locate regions of…
F-CAM: Full Resolution Class Activation Maps via Guided Parametric Upscaling
Soufiane Belharbi, Aydin Sarraf, Marco Pedersoli +3
Class Activation Mapping (CAM) methods have recently gained much attention for weakly-supervised object localization (WSOL) tasks. They allow for CNN visualization and interpretati…
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-…
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…
Min-max Entropy for Weakly Supervised Pointwise Localization
Soufiane Belharbi, Jérôme Rony, Jose Dolz +3
Pointwise localization allows more precise localization and accurate interpretability, compared to bounding box, in applications where objects are highly unstructured such as in me…