Anatomy Contextualized Adaption of CT Foundation Models
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TL;DR - Anatomy Contextualized Adaptation (ACA) efficiently adapts frozen CT foundation models for anatomy-level vision-language alignment while retaining whole-scan context. It improves zero-shot finding classification on Merlin and CT-RATE with under one hour of training after embedding caching.
- TotalSegmentator decomposes CT volumes into anatomy-level embeddings.
- A transformer models cross-anatomy relationships and aligns embeddings with anatomy-specific and scan-level report text.
- ACA outperforms frozen foundation-model baselines and existing fine-grained methods.
- Learned attention patterns suggest plausible routing of context across anatomical regions.
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Anatomy Contextualized Adaption of CT Foundation Models
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TL;DR - Anatomy Contextualized Adaptation (ACA) efficiently adapts frozen CT foundation models for anatomy-level vision-language alignment while retaining whole-scan context. It improves zero-shot finding classification on Merlin and CT-RATE with under one hour of training after embedding caching.
- TotalSegmentator decomposes CT volumes into anatomy-level embeddings.
- A transformer models cross-anatomy relationships and aligns embeddings with anatomy-specific and scan-level report text.
- ACA outperforms frozen foundation-model baselines and existing fine-grained methods.
- Learned attention patterns suggest plausible routing of context across anatomical regions.