通过特化基础图像模型生成胸部X光反事实图像
Generating Chest X-Ray Counterfactuals by Specialising Foundation Image Models
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中文总结 AI 辅助
本文提出特化框架，将预训练生成模型适配为因果模型，训练RadCF模型生成胸部X光反事实图像，在三个数据集上验证其反事实合理性并缓解下游分类器的捷径学习。
中文摘要 AI 辅助
反事实图像生成回答了在回顾性、假设性场景下主体会如何呈现的问题。近期方法提升了感知质量、身份保持以及对底层因果模型的忠实度，但其在医疗领域的应用受限于标注数据稀缺、数据集之间的分布偏移，以及预训练生成模型与反事实推断所需模型之间的不匹配。我们提出特化（specialisation），一种数据和参数高效的框架，用于在分布偏移下将预训练的非因果生成模型适配为因果机制。基于该框架，我们利用潜在流匹配训练了一个放射学反事实图像生成模型，称为RadCF。我们在三个胸部X光数据集上验证了我们的方法，这些数据集涵盖了不同的数据集偏移、数据量和反事实问题，并涉及具有挑战性的高度局部化干预。我们的结果表明，RadCF和特化方法在反事实合理性上优于现有方法，同时具有数据和参数效率，并且生成的反事实图像能够检测并缓解下游医学分类器中的捷径学习。代码可在以下网址获取：此https URL。
英文摘要
Counterfactual image generation answers questions about how a subject would have looked under retrospective, hypothetical scenarios. Recent methods have improved perceptual quality, identity preservation and faithfulness to an underlying causal model, but their adoption in healthcare is limited by scarce annotated data, distribution shift between datasets, and mismatches between pretrained generative models and those required for counterfactual inference. We propose specialisation, a data and parameter-efficient framework for adapting pretrained, non-causal generative models into causal mechanisms under distribution shift. Based on this framework, we train a radiology counterfactual image generation model, called RadCF, using latent flow matching. We validate our approach on three chest X-ray datasets spanning different dataset shifts, data volumes, and counterfactual questions, associated with challenging, highly-localised interventions. Our results show that RadCF and specialisation improve counterfactual soundness over existing methods while being data and parameter efficient, and that the resulting counterfactuals can detect and mitigate shortcut learning in a downstream medical classifier. Code is available at https://github.com/GSK-AI/RadCF/.
发表机构
- GSK（葛兰素史克）
- Imperial College London（帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。
