基于乳腺超声视频流和自监督对比学习的肿瘤良恶性分类系统OA北大核心CSTPCD

Abstact:Breast ultrasound is widely used in the diagnosis of breast tumors.Deep learning-based tumor benign-malignant classification models effectively assist doctors in diagnosis,improving efficiency and reducing misdiagnosis rates,among other benefits.However,the high cost of annotated data limits the development and application of such models.In this study,we construct an unlabeled pretraining dataset from breast ultrasound videos,which includes 11805 target samples and dynamical-ly generated positive and negative sample datasets(with sample sizes of 188880 and 1310355,respectively).Based on this dataset,we build a triplet network and conduct self-supervised contrastive learning.Additionally,we develope Hard Negative Mining and Hard Positive Mining methods to select challenging positive and negative samples for constructing the contrastive loss function,accelerating model convergence.After parameter transfer,the triplet network is fine-tuned and tested on the SYU dataset.Experimental results demonstrate that the triplet network model developed in this study exhibits stronger gen-eralization capability and better classification performance compared to several state-of-the-art models pretrained on ImageNet and previous multi-view contrastive models for breast ultrasound.Furthermore,we test the minimum requirement of annotat-ed data for the model and find that using only 96 annotated data points achieves a performance with an AUC=0.901 and sen-sitivity of 0.835.