Semantic Tree Driven Thyroid Ultrasound Report Generation by Voice Input

The automatic speech recognition has achieved quite good performance in the medical domain in the past several years. However, it is still lacking of enough practical solutions with considering the characteristics of real applications. In this work, we develop an approach to automatically generate semantic-coherent ultrasound reports with voice input. The solution includes key algorithms based on a proposed semantic tree structure. The radiologists do not need to follow the fixed templates. They just need to speak their specific observations for individual patients. We have carried out a set of experiments against a real world thyroid ultrasound dataset with more than 40,000 reports from a reputable hospital in Shanghai, China. The experimental results show that our proposed solution can generate concise and accurate reports.