---
title: "CT基础模型的解剖学上下文化适应：轻量级框架ACA"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.27154v1"
permalink: "https://feed.astralor.com/items/itm_cc506801364be66f303535e1"
published: "2026-07-29T17:32:57.000Z"
score: 81
channel: "ai"
tags: ["ACA","CT视觉语言基础模型","TotalSegmentator","浙江大学","零样本发现分类"]
---

# CT基础模型的解剖学上下文化适应：轻量级框架ACA

## AI 点评

> 这篇论文提出了一个非常实用的轻量级框架ACA，特别适合计算资源有限的医疗AI团队。它将CT基础模型的全局表征与解剖级细粒度对齐巧妙结合，训练效率极高。对于从事医学影像分析的研究者来说，ACA提供了一个可直接部署的基线方案，尤其值得关注零样本发现分类任务的提升。

## 速览

CT视觉语言基础模型在下游任务中表现优异，但常使用整体体积表示，稀释了细粒度解剖信号。现有细粒度预训练虽对齐解剖级特征与文本，却丢弃了全局上下文且计算开销大。本文提出解剖学上下文化适应（ACA），一种轻量级框架，通过TotalSegmentator分解CT体积为解剖级嵌入，经跨解剖关系变换器精炼，并与放射学报告中的解剖级和扫描级文本对齐。在Merlin和CT-RATE上，ACA在零样本发现分类中一致优于冻结基础模型和现有细粒度方法，训练时间少于1小时，同时注意力权重显示合理的跨解剖上下文路由。

## 关键点

- ACA通过TotalSegmentator将CT体积分解为解剖级嵌入，保留细粒度信号。
- 采用跨解剖关系变换器捕获解剖间关系并增强全局上下文。
- 在零样本发现分类中优于冻结基础模型和现有细粒度方法，训练时间少于1小时。
- 注意力权重揭示了合理的跨解剖上下文路由机制。

## 全文

CT vision-language foundation models have demonstrated promising performance across downstream tasks, but are typically trained with whole-volume representations that dilute fine-grained anatomical signals. Fine-grained vision-language pre-training addresses this by aligning anatomy-level visual features with anatomy-specific text, but in doing so discards the global context that whole-volume models provide. Furthermore, existing fine-grained approaches train from scratch, making them computationally expensive. We introduce Anatomy Contextualized Adaptation (ACA), a lightweight framework that adapts frozen CT foundation model representations for anatomy-level vision-language alignment while enhancing global contextualization. ACA uses TotalSegmentator to decompose CT volumes into anatomy-level embeddings, which are refined via a transformer that captures cross-anatomy relationships, and aligned to both per-anatomy and scan-level text extracted from radiology reports. Evaluated on Merlin and CT-RATE, ACA consistently outperforms both the frozen foundation model baselines and existing fine-grained methods in zero-shot finding classification, while requiring less than one hour of training once embeddings are cached. The attention weights learned by ACA's inter-anatomy transformer additionally indicate plausible cross-anatomy context routing. Altogether, these results support ACA as a lightweight approach for adapting CT foundation models to anatomically grounded vision-language alignment while preserving and enhancing global anatomical context.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_cc506801364be66f303535e1