Anatomy Contextualized Adaptation of CT Foundation Models: A Lightweight Framework ACA
Score
81提出了一种新颖的轻量级框架ACA，解决了CT基础模型细粒度与全局上下文的权衡问题，信息增量高。
方法描述清晰，实验设计严谨，结果对比全面，但仅涉及两个数据集，泛化性需进一步验证。
训练效率高（<1小时），可直接应用于临床CT影像分析，实用价值显著。
arXiv时间戳为2025年7月，非常及时，符合当前CT基础模型微调的研究热点。
来自arXiv预印本平台，论文作者机构为浙江大学，较为可靠。
Base 89.5 × T2 Source weight ×0.9 = 81
Curation line 70Selected
Why selected
The paper demonstrates strong innovation in method design, thorough experimental results with notable efficiency, and practical value for the medical imaging domain.
AI commentary
「This paper presents a highly practical lightweight framework ACA, especially suitable for medical AI teams with limited compute resources. It elegantly combines global representation of CT foundation models with anatomy-level fine-grained alignment, achieving remarkable training efficiency. For researchers in medical image analysis, ACA offers a deployable baseline, particularly worth noting for its improvement in zero-shot finding classification.」
Quick overview
CT vision-language foundation models show promising performance in downstream tasks but typically use whole-volume representations that dilute fine-grained anatomical signals. Existing fine-grained approaches align anatomy-level features with text but discard global context and are computationally expensive. This paper proposes Anatomy Contextualized Adaptation (ACA), a lightweight framework that decomposes CT volumes into anatomy-level embeddings via TotalSegmentator, refines them with a cross-anatomy transformer, and aligns with per-anatomy and scan-level text from radiology reports. On Merlin and CT-RATE, ACA consistently outperforms frozen baselines and fine-grained methods in zero-shot finding classification, requiring under one hour of training, with attention weights indicating plausible cross-anatomy context routing.
- ACA decomposes CT volumes into anatomy-level embeddings via TotalSegmentator, preserving fine-grained signals.
- It uses a cross-anatomy transformer to capture inter-anatomy relationships and enhance global context.
- Outperforms frozen baselines and existing fine-grained methods in zero-shot finding classification with under one hour of training.
- Attention weights reveal plausible cross-anatomy context routing mechanisms.
Full text
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