关键词: 多维度, 深度学习, 卷积神经网络, 深度可分离, 染色校正, 病理图像, 病灶

Abstract:

Time-consuming artificial diagnosis of pathological images will cause visual fatigue of doctors, while both misdiagnosis and missed diagnosis are easy to occur. In response to the above phenomena, a method combining multidimensional features of convolutional neural network is proposed to quickly and accurately identify lesion in pathological images. ROI extraction and image cutting is used to obtain small-scale block data. The method of stain correction is used to solve the problems of uneven staining and weak contrast in block data. A deep learning model is built, using several depthwise separable convolution to extract features of different dimensions, adding residual connection to avoid gradient disappearance, combining the feature information of different dimensions to improve feature utilization. The experimental results show that stain correction can improve prediction accuracy and the above model has the characteristics of few parameters and strong robustness. At the same time, the accuracy of lesion recognition in pathological images can reach a high level, while both false positive rate and false negative rate are low, so it will have a broad application prospect in the future.

Key words: multidimensional, deep learning, convolutional neural network, depthwise separable, stain correction, pathological image, lesion