Medical report generation is one of the most challenging tasks in medical
image analysis. Although existing approaches have achieved promising results,
they either require a predefined template database in order to retrieve
sentences or ignore the hierarchical nature of medical report generation. To
address these issues, we propose MedWriter that incorporates a novel
hierarchical retrieval mechanism to automatically extract both report and
sentence-level templates for clinically accurate report generation. MedWriter
first employs the Visual-Language Retrieval~(VLR) module to retrieve the most
relevant reports for the given images. To guarantee the logical coherence
between sentences, the Language-Language Retrieval~(LLR) module is introduced
to retrieve relevant sentences based on the previous generated description. At
last, a language decoder fuses image features and features from retrieved
reports and sentences to generate meaningful medical reports. We verified the
effectiveness of our model by automatic evaluation and human evaluation on two
datasets, i.e., Open-I and MIMIC-CXR.

MedWriter 利用新型层次检索机制自动提取临床准确的医学报告，包括报告和句子级模板，以生成有意义的医学报告。通过 Open-I 和 MIMIC-CXR 的自动评估和人工评估验证了模型的有效性。