电子科技2026，Vol.39Issue(10)：78-86,9.DOI:10.16180/j.cnki.issn1007-7820.2026.10.009
融合SimAM注意力与自监督对比学习的脑出血图像分类
Intracerebral Hemorrhage Image Classification Fusing SimAM Attention with Self-Supervised Contrastive Learning
摘要
Abstract
ICH(Intracerebral Hemorrhage)is the main cause of stroke-related deaths,seriously affecting the survival rate and quality of life of patients.In view of the problems of difficult training and insufficient migration of deep learning in the recognition of cerebral hemorrhage,this study proposes a SimCoNet(Simple Attention Module and Self-Supervised Contrastive Learning Network)network model that integrates a SimAM(Simple,Parameter-Free Attention Module)and self-supervised contrastive learning.Good transferable feature representations are ob-tained through supervised pre-training on natural image datasets and self-supervised learning in combination with large-scale unlabeled cerebral hemorrhage datasets.Introduce the SimAM attention module and fine-tune the clas-sification on the small cerebral hemorrhage dataset through transfer learning.The experimental results show that the accuracy rate of the proposed method in the classification task is 97.9%,which is 0.2 percentage points higher than that of the model without introducing the SimAM attention module.The proposed method overcomes the limitations of small data volume and difficult annotation of medical images,providing a new idea for the diagnosis of cerebral hemorrhage and computer-aided medical diagnosis.关键词
监督学习/迁移学习/深度学习/对比学习/自监督学习/注意力机制/医学图像分类/ResNet50Key words
supervised learning/transfer learning/deep learning/contrastive learning/self-supervised learning/attention mechanisms/medical image classification/ResNet50分类
信息技术与安全科学引用本文复制引用
金运龙,李一鸣,宋燕,孔平..融合SimAM注意力与自监督对比学习的脑出血图像分类[J].电子科技,2026,39(10):78-86,9.基金项目
国家自然科学基金(62073223) （62073223）
上海市自然科学基金(22ZR1443400) National Natural Science Foundation of China(62073223) （22ZR1443400）
Shanghai Natural Science Foundation(22ZR1443400) （22ZR1443400）
