4 papers
Leveraging whole slide difficulty in Multiple Instance Learning to improve prostate cancer grading
Marie Arrivat, Rémy Peyret, Elsa Angelini +1
Multiple Instance Learning (MIL) has been widely applied in histopathology to classify Whole Slide Images (WSIs) with slide-level diagnoses. While the ground truth is established b…
Unsupervised Domain Adaptation with Target-Only Margin Disparity Discrepancy
Gauthier Miralles, Loïc Le Folgoc, Vincent Jugnon +1
In interventional radiology, Cone-Beam Computed Tomography (CBCT) is a helpful imaging modality that provides guidance to practicians during minimally invasive procedures. CBCT dif…
Reducing Variability of Multiple Instance Learning Methods for Digital Pathology
Ali Mammadov, Loïc Le Folgoc, Guillaume Hocquet +1
Digital pathology has revolutionized the field by enabling the digitization of tissue samples into whole slide images (WSIs). However, the high resolution and large size of WSIs pr…
Self-Supervision Enhances Instance-based Multiple Instance Learning Methods in Digital Pathology: A Benchmark Study
Ali Mammadov, Loic Le Folgoc, Julien Adam +4
Multiple Instance Learning (MIL) has emerged as the best solution for Whole Slide Image (WSI) classification. It consists of dividing each slide into patches, which are treated as…