ICON: Improving Inter-Report Consistency of Radiology Report Generation
  via Lesion-aware Mix-up Augmentation
Wenjun Hou Yi Cheng Kaishuai Xu Yan Hu Wenjie Li Jiang Liu
Previous research on radiology report generation has made significant
progress in terms of increasing the clinical accuracy of generated reports. In
this paper, we emphasize another crucial quality that it should possess, i.e.,
inter-report consistency, which refers to the capability of generating
consistent reports for semantically equivalent radiographs. This quality is
even of greater significance than the overall report accuracy in terms of
ensuring the system's credibility, as a system prone to providing conflicting
results would severely erode users' trust. Regrettably, existing approaches
struggle to maintain inter-report consistency, exhibiting biases towards common
patterns and susceptibility to lesion variants. To address this issue, we
propose ICON, which improves the inter-report consistency of radiology report
generation. Aiming at enhancing the system's ability to capture the
similarities in semantically equivalent lesions, our approach involves first
extracting lesions from input images and examining their characteristics. Then,
we introduce a lesion-aware mix-up augmentation technique to ensure that the
representations of the semantically equivalent lesions align with the same
attributes, by linearly interpolating them during the training phase. Extensive
experiments on three publicly available chest X-ray datasets verify the
effectiveness of our approach, both in terms of improving the consistency and
accuracy of the generated reports.