arXiv:2603.18846v3 Announce Type: replace-cross 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 \model, 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-making process. Additionally, \model{} incorp
The increasing deployment of foundation models in critical domains like healthcare necessitates addressing interpretability challenges for ethical and safety reasons.
This development addresses a key limitation of current AI, enabling more trustworthy and auditable applications in high-stakes fields where explainability is crucial.
The ability to develop inherently interpretable foundation models for medical imaging changes the paradigm from 'black box' AI to transparent decision-making.
- · Medical AI developers
- · Healthcare providers
- · Patients
- · AI ethics and regulation bodies
- · Opaque AI systems
- · Companies relying solely on non-interpretable models
Increased adoption of AI in diagnostics due to improved trust and regulatory compliance.
Development of new AI-driven diagnostic tools that can be more easily scrutinized and approved.
Potential for broader public acceptance and integration of AI into other sensitive decision-making processes beyond medicine.
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Read at arXiv cs.LG