基于深度学习的宫颈癌异常细胞快速检测方法

Fast detection method for cervical cancer abnormal cells based on deep learning

宫颈癌是严重危害妇女健康的恶性肿瘤,威胁着女性的生命,而通过基于图像处理的细胞学筛查是癌前筛查的最为广泛的检测方法.近年来,随着以深度学习为代表的机器学习理论的发展,卷积神经网络以其强有效的特征提取能力取得了图像识别领域的革命性突破,被广泛应用于宫颈异常细胞检测等医疗影像分析领域.但由于病理细胞图像具有分辨率高和尺寸大的特点,且其大多数局部区域内都不含有细胞簇,深度学习模型采用穷举候选框的方法进行异常细胞的定位和识别时,经过穷举候选框获得的子图大部分都不含有细胞簇.当子图数量逐渐增加时,大量不含细胞簇的图像作为目标检测网络输入会使图像分析过程存在冗余时长,严重减缓了超大尺寸病理图像分析时的检测速度.本文提出一种新的宫颈癌异常细胞检测策略,针对使用膜式法获得的病理细胞图像,通过基于深度学习的图像分类网络首先判断局部区域是否出现异常细胞,若出现则进一步使用单阶段的目标检测方法进行分析,从而快速对异常细胞进行精确定位和识别.实验表明,本文提出的方法可提高一倍的宫颈癌异常细胞检测速度.

Cervical cancer is a malignant tumor that highly endangers women’s lives.Cytological screening based on image processing is the most widely used detection method for precancerous screening.Recently,with the development of machine learning theory based on deep learning,the convolutional neural network has made a revolutionary breakthrough in the field of image recognition due to its strong and effective extraction ability.In addition,it has been widely used in the field of medical image analysis such as cervical abnormal cell detection.However,due to the characteristic high resolution and large size of pathological cell images,most of its local areas do not contain cell clusters.Moreover,when the deep learning model uses the method of exhausting candidate boxes to locate and identify abnormal cells,most of the sub-images obtained do not contain cell clusters.When the number of sub-images increases gradually,a large number of images without cell clusters as input to the object detection network will make the image analysis process redundant for a long time,which drastically slows down the speed of detection of the large-scale pathological image analysis.In view of this,this paper proposed a new detection strategy for abnormal cells in cervical cancer microscopic imaging.According to the pathological cell images obtained by the membrane method,the image classification network based on deep learning was first used to determine whether there were abnormal cells in the local area.If there were abnormal cells in the local area,the single-stage object detection method was used for further pathological cell image analysis,so that the abnormal cells in the images could be quickly and accurately located and identified.Experimental results show that the proposed method can double the speed of detection of cervical cancer abnormal cells.