In recent years, computer-aided diagnosis systems have shown great potential
in assisting radiologists with accurate and efficient medical image analysis.
This paper presents a novel approach for bone pathology localization and
classification in wrist X-ray images using a combination of YOLO (You Only Look
Once) and the Shifted Window Transformer (Swin) with a newly proposed block.
The proposed methodology addresses two critical challenges in wrist X-ray
analysis: accurate localization of bone pathologies and precise classification
of abnormalities. The YOLO framework is employed to detect and localize bone
pathologies, leveraging its real-time object detection capabilities.
Additionally, the Swin, a transformer-based module, is utilized to extract
contextual information from the localized regions of interest (ROIs) for
accurate classification.

该论文提出了一种新颖的方法，结合 YOLO 和 Swin 以及新提出的模块，用于定位和分类腕部 X 射线图像中的骨病理，解决了精确定位骨病理和准确分类异常的挑战。