2 papers
cs.CV2025
A Multicenter Benchmark of Multiple Instance Learning Models for Lymphoma Subtyping from HE-stained Whole Slide Images
Rao Muhammad Umer, Daniel Sens, Jonathan Noll +12
Timely and accurate lymphoma diagnosis is essential for guiding cancer treatment. Standard diagnostic practice combines hematoxylin and eosin (HE)-stained whole slide images with i…
cs.CV2023
BEL: A Bag Embedding Loss for Transformer enhances Multiple Instance Whole Slide Image Classification
Daniel Sens, Ario Sadafi, Francesco Paolo Casale +2
Multiple Instance Learning (MIL) has become the predominant approach for classification tasks on gigapixel histopathology whole slide images (WSIs). Within the MIL framework, singl…