Streamline pathology foundation model by cross-magnification distillation

arXiv:2509.23097 · cs.CV · Submitted 2025-09-27 · Read on arXiv

cs.CV

Submitted: 2025-09-27

Updated: 2026-10-11

Code: https://github.com/AI4Path-Lab/XMAG

Project page: https://bmirds.github.io/KidneyCancer

Terminology

• Distillation — 4× in this paper · explained in 2 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• AI Integration — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Generalization — 1× in this paper · explained in 13 episodes

Sources

• Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation
• Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology
• Distilling the Knowledge in a Neural Network
• A Stitch in Time: Learning Procedural Workflow via Self-Supervised Plackett-Luce Ranking
• Unleashing Foundation Vision Models: Adaptive Transfer for Diverse Data-Limited Scientific Domains
• A Simple Recipe for Competitive Low-compute Self supervised Vision Models
• SPIDER: A Comprehensive Multi-Organ Supervised Pathology Dataset and Baseline Models
• LEMON: A Large Endoscopic MONocular Dataset and Foundation Model for Perception in Surgical Settings
• Towards Robust Foundation Models for Digital Pathology

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