国家自然科学基金(82174528); 山东省中医药科技项目(2021M146); 山东省研究生教育质量提升计划(SDYKC19147)
目前乳腺癌已取代肺癌成为年发病率最高的癌症, 基于深度学习的目标检测技术可对乳腺X线、乳腺超声和乳腺核磁共振等非侵入式成像进行自动病变检测, 已成为乳腺癌辅助诊断的首选途径. YOLO (you only look once)系列算法是基于深度学习的目标检测算法, 经典YOLO算法在速度和精准度具有优势, 被广泛应用于计算机视觉各领域, 最新YOLO算法是计算机视觉领域的SOTA (state of the art)模型, 如何利用YOLO系列算法提高乳腺癌检测速度和准确率, 已经成为研究者关注的焦点之一. 基于此, 本文介绍经典YOLO系列算法的原理, 梳理经典YOLO系列算法在乳腺癌图像检测中的应用现状, 并归纳总结现存问题, 同时对YOLO系列算法在乳腺癌检测的进一步应用进行展望.
At present, breast cancer, with the highest annual incidence, has replaced lung cancer, and the target detection technology based on deep learning can automatically detect lesions on non-invasive imaging such as mammography X-ray, breast ultrasound, and breast magnetic resonance imaging (MRI), and it has become the preferred way for adjuvant diagnosis of breast cancer. You only look once (YOLO) series algorithms are object detection algorithms based on deep learning, and classical YOLO algorithms have certain advantages in speed and accuracy and are widely used in computer vision fields. The latest YOLO algorithm is the state of the art (SOTA) model in the field of computer vision, and how to use YOLO series algorithms to improve the speed and accuracy of breast cancer detection has become one of the focus of researchers. On this basis, this study introduces the principle of the classical YOLO series algorithms, sorts out the application status of the classical YOLO series algorithms in breast cancer image detection, summarizes the existing problems, and looks forward to the further application of the YOLO series algorithms in breast cancer detection.