发表机构
University of Bamberg（班贝格大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MoPET作为参数高效混合专家模型，通过稀疏路由整合多任务PEFT专家，在MedMNIST基准上实现了医学图像分类准确率提升，缓解了跨域梯度冲突与负迁移问题。
AI 中文摘要
将深度学习模型适配深刻的临床异质性通常依赖参数高效微调（PEFT），以避免与全端到端网络更新相关的严重过拟合。尽管PEFT可成功应对有限数据场景，但它本质上要求为每个特定诊断任务训练独立的适配器。将这些独立适配器整合到单个通用网络中可能会出现负迁移，因为来自冲突视觉域的优化梯度会相互干扰。为解决该问题，我们提出MoPET，这是一种混合专家（MoE）方法，它使用学习到的稀疏路由将每个输入导向注入冻结基础模型的低秩PEFT专家的小子集，在跨数据集共享容量的同时限制跨域梯度冲突。通过在MedMNIST基准上进行选定评估，我们首先确定PEFT优于全网络更新，将平均准确率从86.50%提升至88.97%；随后表明单个MoPET模型可将四个异构数据集整合到一个网络中，相较于最佳独立PEFT适配器，平均准确率从92.83%提升至93.46%；最后表明与辅助数据集联合训练可提升数据受限临床目标的准确率，将平均目标准确率从最强独立适配器的81.58%提升至83.58%。我们的源代码公开于此https URL。
英文摘要
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 experts injected into a frozen foundation model, sharing capacity across datasets while limiting cross-domain gradient conflict. Through selected evaluations on the MedMNIST benchmark, we first establish that PEFT outperforms full network updates, improving average accuracy from 86.50% to 88.97%. We then show that a single MoPET model consolidates four heterogeneous datasets into one network, improving average accuracy over the best isolated PEFT adapters (93.46% versus 92.83%). Finally, we show that co-training with auxiliary datasets improves accuracy on data-constrained clinical targets, raising average target accuracy over the strongest isolated adapter from 81.58% to 83.58%. Our source code is publicly available at https://github.com/sdoerrich97/mopet.
CommentsAccepted to EMA4MICCAI 2026
