Simulating doctors' thinking logic for chest X-ray report generation via Transformer-based Semantic Query learning.

Medical report generation can be treated as a process of doctors' observing, understanding, and describing images from different perspectives. Following this process, this paper innovatively proposes a Transformer-based Semantic Query learning paradigm (TranSQ). Briefly, this paradigm is to learn an intention embedding set and make a semantic query to the visual features, generate intent-compliant sentence candidates, and form a coherent report. We apply a bipartite matching mechanism during training to realize the dynamic correspondence between the intention embeddings and the sentences to induct medical concepts into the observation intentions. Experimental results on two major radiology reporting datasets (i.e., IU X-ray and MIMIC-CXR) demonstrate that our model outperforms state-of-the-art models regarding generation effectiveness and clinical efficacy. In addition, comprehensive ablation experiments fully validate the TranSQ model's innovation and interpretation. The code is available at https://github.com/zjukongming/TranSQ. • We proposed a novel Transformer-based semantic query model for MRG task. • We propose a bipartite matching-based intention embedding learning strategy. • The proposed TranSQ achieves SOTA performance on two well-known MRG benchmarks. • The proposed TranSQ provides accurate sentence-level interpretation information. [ABSTRACT FROM AUTHOR]