Scaling Multi-Teacher Distillation for Digital Pathology
Read the original on arXiv Computer Vision →The paper introduces a scalable multi-teacher distillation (MTD) framework for digital pathology, addressing the poor performance of standard MTD when many teachers are used. By learning teacher‑specific representations through techniques such as learnable teacher tokens, a tailored attention scheme, mixture‑of‑experts layers, and a contrastive loss, the method scales effectively, enabling compact student encoders that are up to 10× more efficient than larger teacher models while matching or surpassing their performance on 39 tile‑ and slide‑level tasks. The authors release a new collection of efficient foundation models, OMNI, trained from ten state‑of‑the‑art teachers and provide an analysis of the complementary teacher‑specific representations.
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