面向可泛化医学图像分割的多模态因果驱动表征学习
Multimodal Causality-Driven Representation Learning for Generalizable Medical Image Segmentation
把因果干预引入 CLIP 式视觉语言模型：用文本提示构建「混淆因子字典」来显式建模设备/成像模式等域偏移，再做因果去混淆，这套「字典+干预」思路可迁移到任何需要跨域泛化的分割或检测任务。
原文依据 construct a confounder dictionary through text prompts, specifically designed to represent domain-specific variations
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论视觉语言模型（VLM），如 CLIP，已在各类计算机视觉任务中展现出显著的零样本能力。然而，由于医学数据的高度变异性和复杂性，将其应用于医学影像仍具挑战性。具体而言，医学图像常因设备差异、操作伪影和成像模式等多种混淆因子而出现显著的域偏移，导致模型在应用于未见域时泛化性能不佳。为解决这一局限，我们提出多模态因果驱动表征学习（MCDRL），这是一个将因果推断与 VLM 相结合的新框架，用于应对医学图像分割中的域泛化问题。MCDRL 分两步实现：首先，它利用 CLIP 的跨模态能力识别候选病灶区域，并通过文本提示构建混淆因子字典，该字典专门用于表示域特定的变化；其次，它训练一个因果干预网络，利用该字典识别并消除这些域特定变化的影响，同时保留对分割任务至关重要的解剖结构信息。大量实验表明，MCDRL 始终优于对比方法，取得了更高的分割精度并展现出稳健的泛化能力。
英文原文摘要 1392 字符
Vision-Language Models (VLMs), such as CLIP, have demonstrated remarkable zero-shot capabilities in various computer vision tasks. However, their application to medical imaging remains challenging due to the high variability and complexity of medical data. Specifically, medical images often exhibit significant domain shifts caused by various confounders, including equipment differences, procedure artifacts, and imaging modes, which can lead to poor generalization when models are applied to unseen domains. To address this limitation, we propose Multimodal Causal-Driven Representation Learning (MCDRL), a novel framework that integrates causal inference with the VLM to tackle domain generalization in medical image segmentation. MCDRL is implemented in two steps: first, it leverages CLIP's cross-modal capabilities to identify candidate lesion regions and construct a confounder dictionary through text prompts, specifically designed to represent domain-specific variations; second, it trains a causal intervention network that utilizes this dictionary to identify and eliminate the influence of these domain-specific variations while preserving the anatomical structural information critical for segmentation tasks. Extensive experiments demonstrate that MCDRL consistently outperforms competing methods, yielding superior segmentation accuracy and exhibiting robust generalizability.