通过因果启发的反事实增强重新思考放射学报告生成
Rethinking Radiology Report Generation via Causal Inspired Counterfactual Augmentation
AI总结：
针对放射学报告生成中疾病共现导致的偏置问题，从因果视角建模混杂因子并提出两种反事实增强策略，有效提升了模型的准确性与泛化能力。
摘要：
放射学报告生成（RRG）作为生物医学领域的视觉与语言交互任务而备受关注。以往的工作继承了传统语言生成任务的理念，旨在生成高可读性的段落作为报告。尽管取得了显著进展，但RRG的一个特定属性——疾病间的独立性被忽视了，导致模型被偏置数据分布带来的疾病共现所混淆，从而生成不准确的报告。本文中，为了重新思考这一问题，我们首先从因果视角对变量间的因果效应进行建模，借此我们证明了在偏置分布上疾病间的共现关系作为混杂因子，通过两条后门路径（即联合视觉耦合和条件序列耦合）干扰了准确性。随后，我们提出了一种新颖的模型无关的反事实增强方法，包含两种策略：基于原型的反事实样本合成（P-CSS）和魔方式反事实报告重构（Cube），以干预后门路径，从而增强RRG模型的准确性和泛化能力。在广泛使用的MIMIC-CXR数据集上的实验结果证明了所提方法的有效性。此外，在IU X-Ray数据集上评估了泛化性能，验证了本工作能有效减少由不同分布引起的共现对结果的影响。
英文摘要：
Radiology Report Generation (RRG) draws attention as a vision-and-language interaction of biomedical fields. Previous works inherited the ideology of traditional language generation tasks, aiming to generate paragraphs with high readability as reports. Despite significant progress, the independence between diseases-a specific property of RRG-was neglected, yielding the models being confused by the co-occurrence of diseases brought on by the biased data distribution, thus generating inaccurate reports. In this paper, to rethink this issue, we first model the causal effects between the variables from a causal perspective, through which we prove that the co-occurrence relationships between diseases on the biased distribution function as confounders, confusing the accuracy through two backdoor paths, i.e. the Joint Vision Coupling and the Conditional Sequential Coupling. Then, we proposed a novel model-agnostic counterfactual augmentation method that contains two strategies, i.e. the Prototype-based Counterfactual Sample Synthesis (P-CSS) and the Magic-Cube-like Counterfactual Report Reconstruction (Cube), to intervene the backdoor paths, thus enhancing the accuracy and generalization of RRG models. Experimental results on the widely used MIMIC-CXR dataset demonstrate the effectiveness of our proposed method. Additionally, a generalization performance is evaluated on IU X-Ray dataset, which verifies our work can effectively reduce the impact of co-occurrences caused by different distributions on the results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
