The field of radiology report generation is witnessing significant advancements, particularly in the integration of multi-modal data and the enhancement of clinical accuracy. Innovations are focusing on leveraging multi-view radiographs, anatomical and pathological information, and segmentation masks to improve the precision and relevance of generated reports. Additionally, there is a growing emphasis on addressing long-tailed data distributions and fairness in diagnostic tools, as well as incorporating expert-derived textual features alongside visual data. Small language models tailored for radiology tasks are also emerging, offering specialized support for various radiology workflows. These developments collectively aim to streamline clinical workloads and enhance the diagnostic capabilities of AI-driven radiology tools.
Precision and Fairness in AI-Driven Radiology Report Generation
Sources
MCL: Multi-view Enhanced Contrastive Learning for Chest X-ray Report Generation
LTCXNet: Advancing Chest X-Ray Analysis with Solutions for Long-Tailed Multi-Label Classification and Fairness Challenges
Anatomy-Guided Radiology Report Generation with Pathology-Aware Regional Prompts
Deep BI-RADS Network for Improved Cancer Detection from Mammograms
MAIRA-Seg: Enhancing Radiology Report Generation with Segmentation-Aware Multimodal Large Language Models
ORID: Organ-Regional Information Driven Framework for Radiology Report Generation
RadPhi-3: Small Language Models for Radiology
Uterine Ultrasound Image Captioning Using Deep Learning Techniques