Representative Image Feature Extraction via Contrastive Learning Pretraining for Chest X-ray Report Generation
This addresses the time-consuming and expertise-dependent task of medical report generation for radiologists, though it appears incremental as it builds on existing contrastive learning methods with domain-specific adaptations.
The authors tackled the problem of generating chest X-ray reports by proposing a contrastive learning framework that pretrains visual encoders specifically for medical images, using lung segmentation as augmentation to focus on relevant regions. Their approach improved report generation performance both quantitatively and qualitatively compared to previous methods.
Medical report generation is a challenging task since it is time-consuming and requires expertise from experienced radiologists. The goal of medical report generation is to accurately capture and describe the image findings. Previous works pretrain their visual encoding neural networks with large datasets in different domains, which cannot learn general visual representation in the specific medical domain. In this work, we propose a medical report generation framework that uses a contrastive learning approach to pretrain the visual encoder and requires no additional meta information. In addition, we adopt lung segmentation as an augmentation method in the contrastive learning framework. This segmentation guides the network to focus on encoding the visual feature within the lung region. Experimental results show that the proposed framework improves the performance and the quality of the generated medical reports both quantitatively and qualitatively.