2 papers
cs.CV2026
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.CV2026
Deep Learning-Based Fixation Type Prediction for Quality Assurance in Digital Pathology
Oskar Thaeter, Tanja Niedermair, Jan E. G. Albin +3
Accurate annotation of fixation type is a critical step in slide preparation for pathology laboratories. However, this manual process is prone to errors, impacting downstream analy…