Towards Interpretable Foundation Models for Retinal Fundus Images
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 fields generate class evidence maps that are faithful to the model's decision-ma
Record details
Published: 3 August 2026
Source: HuggingFace Daily Papers
Category: Research
Topics: Safety & alignment · Healthcare
Retrieved: 11 August 2026
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How to cite this record
ethics.ai (3 August 2026), “Towards Interpretable Foundation Models for Retinal Fundus Images,” evidence record 17984, https://ethics.ai/record/17984 (originally published by HuggingFace Daily Papers).
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