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Echocardiography is widely used to clinical practice for diagnosis andtreatment, e.g., on the common congenital heart defects. The traditional manualmanipulation is error-prone due to the staff shortage, excess workload, andless experience, leading to the urgent requirement of an automatedcomputer-aided reporting system to lighten the workload of ultrasonologistsconsiderably and assist them in decision making. Despite some recent successfulattempts in automatical medical report generation, they are trapped in theultrasound report generation, which involves unstructured-view images andtopic-related descriptions. To this end, we investigate the task of theunstructured-view topic-related ultrasound report generation, and propose anovel factored attention and embedding model (termed FAE-Gen). The proposedFAE-Gen mainly consists of two modules, i.e., view-guided factored attentionand topic-oriented factored embedding, which 1) capture the homogeneous andheterogeneous morphological characteristic across different views, and 2)generate the descriptions with different syntactic patterns and differentemphatic contents for different topics. Experimental evaluations are conductedon a to-be-released large-scale clinical cardiovascular ultrasound dataset(CardUltData). Both quantitative comparisons and qualitative analysisdemonstrate the effectiveness and the superiority of FAE-Gen over sevencommonly-used metrics.

Factored Attention and Embedding for Unstructured-view Topic-related Ultrasound Report Generation