  Automated radiology report generation aims to generate radiology reports that
contain rich, fine-grained descriptions of radiology imaging. Compared with
image captioning in the natural image domain, medical images are very similar
to each other, with only minor differences in the occurrence of diseases. Given
the importance of these minor differences in the radiology report, it is
crucial to encourage the model to focus more on the subtle regions of disease
occurrence. Secondly, the problem of visual and textual data biases is serious.
Not only do normal cases make up the majority of the dataset, but sentences
describing areas with pathological changes also constitute only a small part of
the paragraph. Lastly, generating medical image reports involves the challenge
of long text generation, which requires more expertise and empirical training
in medical knowledge. As a result, the difficulty of generating such reports is
increased. To address these challenges, we propose a disease-oriented retrieval
framework that utilizes similar reports as prior knowledge references. We
design a factual consistency captioning generator to generate more accurate and
factually consistent disease descriptions. Our framework can find most similar
reports for a given disease from the CXR database by retrieving a
disease-oriented mask consisting of the position and morphological
characteristics. By referencing the disease-oriented similar report and the
visual features, the factual consistency model can generate a more accurate
radiology report.
