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Found 6 papers, 5 papers with code
Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation
To address this gap, we established a most comprehensive benchmark to evaluate the performance of off-the-shelf foundation models across six distinct clinical task types, encompassing a total of 39 specific tasks.
A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model
To our knowledge, this is the first attempt to incorporate multimodal knowledge at the slide level for enhancing pathology FMs, expanding the modelling context from unimodal to multimodal knowledge and from patch-level to slide-level.
Visual Mamba: A Survey and New Outlooks
Mamba, a recent selective structured state space model, excels in long sequence modeling, which is vital in the large model era.
GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist Collaboration
Specifically, we propose a cooperative framework, Generalist-Specialist Collaboration (GSCo), which consists of two stages, namely the construction of GFM and specialists, and collaborative inference on downstream tasks.
MambaMIL: Enhancing Long Sequence Modeling with Sequence Reordering in Computational Pathology
Multiple Instance Learning (MIL) has emerged as a dominant paradigm to extract discriminative feature representations within Whole Slide Images (WSIs) in computational pathology.
HistGen: Histopathology Report Generation via Local-Global Feature Encoding and Cross-modal Context Interaction
Histopathology serves as the gold standard in cancer diagnosis, with clinical reports being vital in interpreting and understanding this process, guiding cancer treatment and patient care.
