arXiv Machine Learning August 3, 2026 By Sebastian Doerrich, Daniel W\"urtinger, Francesco Di Salvo, Shyam Nandan Rai, Christian Ledig

MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification

Read the original on arXiv Machine Learning →

arXiv:2607. 29462v1 Announce Type: cross Abstract: Adapting deep learning models to profound clinical heterogeneity typically relies on parameter-efficient fine-tuning (PEFT) to avoid the severe overfitting associated with full end-to-end network updates.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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OPERA: Offline Policy-guided Expert Routing and Adaptation for Universal Biomedical Image Analysis

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