DistillPath: An Efficient 22M Distilled Pathology Encoder Approaching Large Foundation Model Performance

Ramon Kaspar; Andrey Ignatov; Valentina Boeva

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arXiv · 2608.17872

DistillPath: An Efficient 22M Distilled Pathology Encoder Approaching Large Foundation Model Performance

Ramon Kaspar·Andrey Ignatov·Valentina Boeva

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Abstract

Many high-performing pathology tile encoders are now foundation models with hundreds of millions to over a billion parameters. Encoding and storing the thousands of tiles in each whole-slide image with such models is costly on commodity hardware, so compact encoders that retain useful downstream performance are a valuable alternative. We present DistillPath-KS16, which starts from the existing 22M kaiko ViT-S/16 encoder and improves it by distilling from released pathology encoders used as frozen teachers. The recipe reads only the teachers' final class and patch tokens and trains on 6,000 public slides, needing neither their DINO nor iBOT pretraining heads nor a billion-tile corpus, so it applies to any released encoder that exposes backbone tokens. We distill four teachers spanning 86M to 1.1B parameters into the same student. Every variant improves the kaiko baseline on all three benchmarks we use, EVA, HEST, and PLISM, and the strongest teacher is task-dependent. On the seven-task EVA mean, DistillPath-KS16-Virchow2 reaches $0.795$, within $0.015$ points of Virchow2, the top-scoring model in our evaluation, at about $29\times$ fewer parameters; it also scores above H0-mini and GPFM on this aggregate metric, though that advantage is task-concentrated rather than uniform. Because it remains a 22M ViT-S/16 with 384-dimensional features, DistillPath-KS16 runs more than $25\times$ faster than Virchow2. Code is available at https://github.com/RamonKaspar/DistillPath, and released model weights are available at https://huggingface.co/collections/RamonK/distillpath.

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Ramon Kaspar, Andrey Ignatov, Valentina Boeva. 2026-08-18. DistillPath: An Efficient 22M Distilled Pathology Encoder Approaching Large Foundation Model Performance. https://arxiv.org/abs/2608.17872

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