机构地区:[1]中国科学技术大学生物医学工程学院(苏州)生命科学与医学部,安徽合肥230026 [2]中国科学院苏州生物医学工程技术研究所,江苏苏州215162 [3]河南赛诺特生物技术有限公司,河南郑州450001
出 处:《中国激光》2023年第15期101-110,共10页Chinese Journal of Lasers
基 金:苏州市基础研究试点项目(SJC2021022)。
摘 要:宫颈异常细胞特征细微难以提取、小目标容易漏检、细胞边界回归不准确导致异常细胞检测精度不高,鉴于此,本文提出了一种结合注意力的全尺度特征融合RetinaNet(AFF-RetinaNet)宫颈异常细胞检测算法.首先,采用ResNeSt-50作为特征提取网络提取宫颈异常细胞的细微特征;其次,引入平衡特征金字塔(BFP)结构,对所有特征层进行全尺度融合,增强小目标的语义信息,并利用BFP中的非局部注意力模块获取图像的全局信息,以进一步增强特征空间的语义信息;最后,采用CIoU Loss作为回归分支的损失函数,以提高对异常细胞边界回归的准确率.另外,针对实际应用场景,基于AFF-RetinaNet算法实现了全视野宫颈细胞病理学图像(WSI)推理流程,并基于该推理流程对WSI中的异常细胞进行了检测.AFF-RetinaNet在宫颈异常细胞数据集上的平均精度均值(mAP)为83.4%,其中对小目标的mAP值(mAP-s)达到了24.4%,相较于基准RetinaNet算法分别提高了3.2个百分点和10.8个百分点.基于AFF-Retina的WSI推理结果在感兴趣区域中的mAP为70.8%.实验结果表明:AFF-RetinaNet算法可以增强对小尺寸异常细胞的检测能力,有效提升宫颈异常细胞的检测精度.基于AFF-RetinaNet的宫颈WSI推理流程可辅助医生快速定位高分辨率宫颈WSI中的异常细胞,有望减轻医生的阅片负担.Objective With the development of digital pathology and artificial intelligence technology,research on the automatic detection of abnormal cervical cells has made great progress.Of the different technologies,object detection technology based on deep learning can simultaneously locate and classify an object,making it a promising application in the field of abnormal cervical cell detection.However,the detection accuracy of abnormal cervical cells still has room for improvement because of the subtle features of abnormal cells that are difficult to extract,small targets that are easily missed,and inaccurate boundary regression.Therefore,in this study,a full-scale feature fusion RetinaNet algorithm combined with attention(AFF-RetinaNet)is proposed for abnormal cervical cell detection to improve the detection accuracy of abnormal cervical cells.In addition,for practical application scenarios,a whole slide image(WSI)inference process based on the AFF-RetinaNet algorithm was implemented for detecting abnormal cells in WSI,which can help pathologists quickly locate abnormal cervical cells in high-resolution WSI and reduce the burden of having to read cervical cytology images.Methods To improve the accuracy of abnormal cervical cell detection,AFF-RetinaNet is proposed.First,ResNeSt-50 was used as the feature extraction network to extract the fine features of abnormal cervical cells.The structure of balanced feature pyramid(BFP)was then applied to integrate all feature layers at full scale and obtain the global information of the image,which can enhance the semantic information of small targets.Finally,CIoU loss was used as the loss function of the regression branch to improve the accuracy of abnormal cell boundary regression.In the WSI inference process based on AFF-RetinaNet,the WSI was first divided into several patches,and AFF-RetinaNet was then used to obtain the detection results of each patch.Finally,all detection results of patches were integrated,and the non-maximum suppression(NMS)algorithm was used as a post-proc
关 键 词:医用光学 宫颈细胞病理图像 目标检测 小目标 特征融合 注意力机制
分 类 号:TP391.4[自动化与计算机技术—计算机应用技术]
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