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示。然而，其中许多模型依赖于可解释性有限的架构，这在医学影像等高风险的领域中是一个关键问题。我们提出Dual-IFM，一种天生具备可解释性的基础模型，体现在两个方面：首先，它通过忠实于决策过程的类别证据图为单个图像提供局部可解释性；其次，它通过允许直接可视化模型表示空间的二维投影层为整个数据集提供全局可解释性。我们在来自不同来源的超过80万张彩色眼底照片上训练模型，以学习面向多种下游任务的通用且可解释的表示。结果表明，我们的模型在参数数量多达16倍的最先进基础模型上达到相似的性能范围，同时能在分布外数据上提供可解释的预测。这一结果提示，大规模自监督预训练与内在可解释性相结合，可为视网膜成像产生稳健的表示。