  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.
