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Automated medical report generation has become increasingly important inmedical analysis. It can produce computer-aided diagnosis descriptions and thussignificantly alleviate the doctors' work. Inspired by the huge success ofneural machine translation and image captioning, various deep learning methodshave been proposed for medical report generation. However, due to the inherentproperties of medical data, including data imbalance and the length andcorrelation between report sequences, the generated reports by existing methodsmay exhibit linguistic fluency but lack adequate clinical accuracy. In thiswork, we propose an image-to-indicator hierarchical transformer (IIHT)framework for medical report generation. It consists of three modules, i.e., aclassifier module, an indicator expansion module and a generator module. Theclassifier module first extracts image features from the input medical imagesand produces disease-related indicators with their corresponding states. Thedisease-related indicators are subsequently utilised as input for the indicatorexpansion module, incorporating the "data-text-data" strategy. Thetransformer-based generator then leverages these extracted features along withimage features as auxiliary information to generate final reports. Furthermore,the proposed IIHT method is feasible for radiologists to modify diseaseindicators in real-world scenarios and integrate the operations into theindicator expansion module for fluent and accurate medical report generation.Extensive experiments and comparisons with state-of-the-art methods undervarious evaluation metrics demonstrate the great performance of the proposedmethod.

IIHT: Medical Report Generation with Image-to-Indicator Hierarchical Transformer