Cellular-Communication-Level Interpretability for Pathology Foundation Models via Graph Distillation on Microenvironment

arXiv:2609.26073 · cs.CV · Submitted 2026-08-02 · Read on arXiv

cs.CV

Submitted: 2026-08-02

Updated: 2026-08-02

Terminology

• Distillation — 25× in this paper · explained in 2 episodes
• Foundation Model — 13× in this paper · explained in 3 episodes
• Embeddings — 12× in this paper · explained in 2 episodes
• Foundation Models — 8× in this paper · explained in 7 episodes
• Interpretability — 6× in this paper · explained in 15 episodes
• Knowledge Distillation — 4× in this paper · explained in 11 episodes
• AUC — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Feature Extraction — 2× in this paper · explained in 2 episodes
• GNNs — 2× in this paper · explained in 2 episodes
• Instance Segmentation — 2× in this paper · explained in 3 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Explainable AI (XAI) — 1× in this paper · explained in 7 episodes
• Graph Construction — 1× in this paper · explained in 3 episodes
• Graph Neural Network (GNN) — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Self-Supervised Learning (SSL) — 1× in this paper · explained in 4 episodes

Sources

• Phikon-v2, A large and public feature extractor for biomarker prediction
• Distilling the Knowledge in a Neural Network
• Histo-Genomic Knowledge Distillation For Cancer Prognosis From Histopathology Whole Slide Images
• Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

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