吉林大学学报（信息科学版）2024，Vol.42Issue(5)：881-888,8.
基于对比学习的医学图像分类改进方法
Improved Method of Medical Images Classification Based on Contrast Learning
摘要
Abstract
Medical image classification is an important method to determine the illness of patients and give corresponding treatment advice.As medical image labeling requires relevant professional knowledge,it is difficult to obtain large-scale medical image classification labels.Andthe development of medical image classification based on deep learning method is limited to some extent.To solve this problem,self-supervised contrast learning is applied to medical image classification tasks in this paper.Contrast learning method is used in pre-training of medical image classification.The features are learned from unlabeled medical images in the pre-training stage to provide prior knowledge for subsequent medical image classification.Experimental results show that the proposed improved method of medical image classification based on self-supervised contrast learning enhances the classification performance of the ResNet.关键词
医学图像/图像分类/自监督学习/深度学习Key words
medical image/image classification/self-supervised learning/deep learning分类
信息技术与安全科学引用本文复制引用
刘世峰,王欣..基于对比学习的医学图像分类改进方法[J].吉林大学学报（信息科学版）,2024,42(5):881-888,8.基金项目
吉林省科技发展计划基金资助项目(20170414006GH （）
20210204138YY) （）
