DeepLOC：基于深度学习的腕部X射线图像骨病变定位与分类
DeepLOC: Deep Learning-based Bone Pathology Localization and Classification in Wrist X-ray Images
AI总结：
本研究提出DeepLOC方法，结合YOLO与带新模块的Swin Transformer，解决腕部X射线骨病变精准定位和异常精确分类的问题，辅助提升医学图像诊断效率与准确性。
摘要：
近年来，计算机辅助诊断系统在辅助放射科医生准确高效地进行医学图像分析方面展现出巨大潜力。本文提出了一种结合YOLO（You Only Look Once，你只看一次）与移位窗口Transformer（Swin）及新提出模块的腕部X射线图像骨病变定位与分类新方法。该方法解决了腕部X射线分析中的两个关键挑战：骨病变的精准定位和异常的精确分类。方法采用YOLO框架，利用其实时目标检测能力检测并定位骨病变。此外，还利用基于transformer的模块Swin从定位后的感兴趣区域（ROIs）中提取上下文信息，以实现准确分类。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
