Multi-scale Fusion of Fast Vision Mamba and Foundation Model Features for Patch-Level Gleason Grading in Computational Pathology

Key points

• Pathology foundation models (FMs) extract strong patch representations but often operate at a single-scale, which limits spatial context and disproportionately affects classes that depend on broader context.1
• We propose a multi-scale classifier built on state-space models.1
• Our method combines two complementary feature sources: a frozen foundation model, and a lightweight FastVim, pre-trained on ImageNet and distilled from the foundation model, that reads the raw image crops.1
• At each scale, a Mamba model encodes the features from both sources with linear-time spatial context, and a jointly trained head fuses the encoded representations into one prediction.1

Sources (1)

• [1]Multi-scale Fusion of Fast Vision Mamba and Foundation Model Features for Patch-Level Gleason Grading in Computational Pathology
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 07:04 AM

  Automated Gleason grading of prostate whole slide images (WSIs) enables faster and more consistent cancer risk stratification.

  Pathology foundation models (FMs) extract strong patch representations but often operate at a single-scale, which limits spatial context and disproportionately affects classes that depend on broader context.

Extractive summary: sentences quoted from the sources.