Explaining BiomedCLIP with Weighted Banzhaf Interactions Supported by Tree-Gram Parsing
Vision-Language Models (VLMs) are demonstrating significant capabilities in medical tasks like radiology analysis, yet providing faithful and interpretable explanations remains a key consideration for their responsible deployment in clinical settings. However, existing explanation methods, such as the widely used FIxLIP framework, often struggle with the fine-grained nature of modern tokenizers. The tokenization problem fragments clinical concepts---splitting terms like "saddle embolus" into scattered, meaningless subwords---which leads to noisy, semantically incoherent cross-modal attributions. Such fragmentation also results in a combinatorial explosion of interaction possibilities, obs...
| Authors | Jakub Rymarski, Adam Rempała, Bartłomiej Sobieski et al. |
|---|---|
| Primary category | cs.CV |
| Published | 25 July 2026 |
| Last updated at source | 25 July 2026 |
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