Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …

Large language models (LLMs) have recently demonstrated their potential in clinical
applications, providing valuable medical knowledge and advice. For example, a large dialog …

Automatic radiology reporting has great clinical potential to relieve radiologists from heavy
workloads and improve diagnosis interpretation. Recently, researchers have enhanced data …

The automatic generation of radiology reports has the potential to assist radiologists in the
time-consuming task of report writing. Existing methods generate the full report from image …

Automatically generating radiology reports can improve current clinical practice in diagnostic
radiology. On one hand, it can relieve radiologists from the heavy burden of report writing; …

In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis,
especially for intricate cases. This inspires us to explore a" multi-expert joint diagnosis" …

The remarkable performance of the Transformer architecture in natural language processing
has recently also triggered broad interest in Computer Vision. Among other merits …

Radiology report generation aims to automatically generate a clinically accurate and
coherent paragraph from the X-ray image, which could relieve radiologists from the heavy …

Medical report generation task, which targets to produce long and coherent descriptions of
medical images, has attracted growing research interests recently. Different from the general …