What Makes High-Magnification Knowledge Transferable? A Study of Cross-Resolution Distillation in Whole-Slide Imaging

arXiv:2609.36407 · cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CV

Submitted: 2026-09-29

Updated: 2026-10-01

Terminology

• Distillation — 126× in this paper · explained in 2 episodes
• Reconstruction Error — 22× in this paper · explained in 2 episodes
• AUC — 16× in this paper · explained in 2 episodes
• Adaptation — 9× in this paper · explained in 1 episode
• Foundation Model — 9× in this paper · explained in 3 episodes
• Knowledge Distillation — 7× in this paper · explained in 11 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Generalization — 6× in this paper · explained in 13 episodes
• Cosine Similarity — 4× in this paper · explained in 3 episodes
• Decode — 3× in this paper · explained in 2 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Action Space — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Calibration — 2× in this paper · explained in 7 episodes
• Department of Computer Science and Software Engineering — 2× in this paper
• Flow Matching — 2× in this paper · explained in 8 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Perplexity — 2× in this paper · explained in 3 episodes
• Redundancy — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Cancer Genome Atlas Program — 1× in this paper
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Low-Rank Adaptation (LoRA) — 1× in this paper · explained in 9 episodes
• Nadia — 1× in this paper · explained in 2 episodes
• Principal Component Analysis (PCA) — 1× in this paper · explained in 4 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• AtlasPatch: Efficient Tissue Detection and High-throughput Patch Extraction for Computational Pathology at Scale
• Gaussian Error Linear Units (GELUs)
• Distilling the Knowledge in a Neural Network
• Unifying distillation and privileged information
• FitNets: Hints for Thin Deep Nets
• DINOv3
• Streamline pathology foundation model by cross-magnification distillation

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