MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification
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. Although PEFT successfully navigates limited data scenarios, it inherently forces the training of a separate, isolated adapter for every specific diagnostic task. Consolidating these isolated adapters into a single generalist network risks negative transfer, as optimization gradients from conflicting visual domains interfere. To address this, we propose MoPET, a mixture-of-experts (MoE) method that uses a learned sparse router to direct each input through a small subset of low-rank PEFT ...
| Authors | Sebastian Doerrich, Daniel Würtinger, Francesco Di Salvo et al. |
|---|---|
| Primary category | eess.IV |
| Published | 31 July 2026 |
| Last updated at source | 3 August 2026 |
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