可解释视网膜眼底图像基础模型DualIFM

AI 摘要

DualIFM是一个为视网膜眼底图像设计的可解释基础模型，采用BagNet骨干网络，通过小感受野生成忠实于模型决策过程的类别证据图，并在预训练期间加入2D投影层以直接可视化表示空间。该模型在超过80万张彩色眼底照片上训练，性能与参数多16倍的RETFound相当，同时提供可解释的预测。代码和预训练模型已在GitHub上公开。

正文节选

Towards Interpretable Foundation Models for Retinal Fundus Images Abstract Foundation models are used to extract transferable representations from large amounts of unlabeled data, typically via self-supervised learning (SSL). However, many of these models rely on architectures that offer limited interpretability, a critical issue in high-stakes domains such as medical imaging. We propose DualIFM, a foundation model that is interpretable-by-design via a BagNet backbone whose small receptive field

发布时间：—

抓取时间：2026-08-10 16:39

来源机构：Hugging Face