Sensors, Vol. 23, Pages 1057: Diversity Learning Based on Multi-Latent Space for Medical Image Visual Question Generation

Sensors, Vol. 23, Pages 1057: Diversity Learning Based on Multi-Latent Space for Medical Image Visual Question Generation Sensors doi: 10.3390/s23031057 Authors: He Zhu Ren Togo Takahiro Ogawa Miki Haseyama Auxiliary clinical diagnosis has been researched to solve unevenly and insufficiently distributed clinical resources. However, auxiliary diagnosis is still dominated by human physicians, and how to make intelligent systems more involved in the diagnosis process is gradually becoming a concern. An interactive automated clinical diagnosis with a question-answering system and a question generation system can capture a patient’s conditions from multiple perspectives with less physician involvement by asking different questions to drive and guide the diagnosis. This clinical diagnosis process requires diverse information to evaluate a patient from different perspectives to obtain an accurate diagnosis. Recently proposed medical question generation systems have not considered diversity. Thus, we propose a diversity learning-based visual question generation model using a multi-latent space to generate informative question sets from medical images. The proposed method generates various questions by embedding visual and language information in different latent spaces, whose diversity is trained by our newly proposed loss. We have also added control over the categories of generated questions, making the generated questions directional. Furthermore, we use...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research