Unifying Relational Sentence Generation and Retrieval for Medical Image Report Composition
This work is significant for medical professionals and researchers in medical AI, as it aims to improve the accuracy and semantic consistency of medical image reports, particularly for rare abnormal descriptions, which is an incremental improvement over existing methods.
This paper addresses the challenge of medical image report composition, which requires accurate medical term diagnosis and diverse information forms. The authors propose a framework that unifies template retrieval and sentence generation, using hybrid-knowledge co-reasoning and an adaptive generation mode to handle both common and rare abnormalities while maintaining semantic consistency among medical terms. Experimental results on two benchmarks show the framework's superiority in human and metric evaluations.
Beyond generating long and topic-coherent paragraphs in traditional captioning tasks, the medical image report composition task poses more task-oriented challenges by requiring both the highly-accurate medical term diagnosis and multiple heterogeneous forms of information including impression and findings. Current methods often generate the most common sentences due to dataset bias for individual case, regardless of whether the sentences properly capture key entities and relationships. Such limitations severely hinder their applicability and generalization capability in medical report composition where the most critical sentences lie in the descriptions of abnormal diseases that are relatively rare. Moreover, some medical terms appearing in one report are often entangled with each other and co-occurred, e.g. symptoms associated with a specific disease. To enforce the semantic consistency of medical terms to be incorporated into the final reports and encourage the sentence generation for rare abnormal descriptions, we propose a novel framework that unifies template retrieval and sentence generation to handle both common and rare abnormality while ensuring the semantic-coherency among the detected medical terms. Specifically, our approach exploits hybrid-knowledge co-reasoning: i) explicit relationships among all abnormal medical terms to induce the visual attention learning and topic representation encoding for better topic-oriented symptoms descriptions; ii) adaptive generation mode that changes between the template retrieval and sentence generation according to a contextual topic encoder. Experimental results on two medical report benchmarks demonstrate the superiority of the proposed framework in terms of both human and metrics evaluation.