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, which is a critical issue in high-stakes domains such as medical imaging. We propose Dual-IFM, a foundation model that is interpretable-by-design in two ways: First, it provides local interpretability for individual images through class evidence maps that are faithful to the decision-making process. Second, it provides global interpretability for entire datasets through a 2D projection layer that allows for direct visualization of the model's representation space. We trained our model on over 800,000 color fundus photography from various sources to learn generalizable, interpretable representations for different downstream tasks. Our results show that our model reaches a performance range similar to that of state-of-the-art foundation models with up to $16\times$ the number of parameters, while providing interpretable predictions on out-of-distribution data. Our results suggest that large-scale SSL pretraining paired with inherent interpretability can lead to robust representations for retinal imaging.

翻译：基础模型通过自监督学习从大量未标注数据中提取可迁移表征，但许多此类模型依赖的架构可解释性有限，这在医学影像等高风险领域是个关键问题。我们提出Dual-IFM，一种从设计上具备双重可解释性的基础模型：首先，通过忠实于决策过程的类别证据图，为单张图像提供局部可解释性；其次，通过支持模型表征空间直接可视化的二维投影层，为整个数据集提供全局可解释性。我们在来自不同数据源的80余万张彩色眼底照片上训练该模型，使其能学习适用于多种下游任务的通用化、可解释表征。结果表明，我们模型的性能与参数量高达其16倍的最优基础模型相近，且能为分布外数据提供可解释预测。本研究提示，大规模自监督预训练与内在可解释性相结合，有望为视网膜成像生成稳健的表征。