8 citations · 20 across the 14 of their papers we have counts for
4 papers · 1 filter
Pathryoshka: Compressing Pathology Foundation Models via Multi-Teacher Knowledge Distillation with Nested Embeddings
Christian Grashei, Christian Brechenmacher, Rao Muhammad Umer +4
Pathology foundation models (FMs) have driven significant progress in computational pathology. However, these high-performing models can easily exceed a billion parameters and prod…
Towards Cellular-Scale Interpretability in Pathology Foundation Models for Biomarker Assessment
Jingsong Liu, Han Li, Zhengyang Xu +19
Molecular biomarker testing in pathology is often costly and tissue-consuming, limiting scalable clinical deployment. Artificial intelligence applied to hematoxylin and eosin (HE)-…
Unit-Based Histopathology Tissue Segmentation via Multi-Level Feature Representation
Ashkan Shakarami, Azade Farshad, Yousef Yeganeh +4
We propose UTS, a unit-based tissue segmentation framework for histopathology that classifies each fixed-size 32 * 32 tile, rather than each pixel, as the segmentation unit. This a…
A Graph-Based Framework for Interpretable Whole Slide Image Analysis
Alexander Weers, Alexander H. Berger, Laurin Lux +3
The histopathological analysis of whole-slide images (WSIs) is fundamental to cancer diagnosis but is a time-consuming and expert-driven process. While deep learning methods show p…