Automatic detection of breast lesions in automated 3D breast ultrasound with cross-organ transfer learning

被引：0
作者：
Lingyun B.A.O. [1 ]
HUANG Z. [2 ]
LIN Z. [2 ]
SUN Y. [2 ]
CHEN H. [3 ]
LI Y. [4 ]
LI Z. [5 ,6 ]
YUAN X. [2 ]
XU L. [7 ]
TAN T. [2 ]
机构：
[1] Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou
[2] Faculty of Applied Sciences, Macao Polytechnic University, Macao
[3] Pathology Department, Changsha First Hospital, Changsha
[4] Radiology Department, Changsha First Hospital, Changsha
[5] College of Aerospace Science and Engineering, National University of Defense Technology, Changsha
[6] Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation, Changsha
[7] School of Information Science and Technology, Shanghaitech University, Shanghai
来源：
Virtual Reality and Intelligent Hardware | 2024年 / 6卷 / 03期
关键词：
Automated 3D breast ultrasound; Breast cancers; Breast ultrasound; Computer-aided diagnosis; Convolutional neural networks; Cross organ learning; Deep learning; Transfer learning;
D O I：
10.1016/j.vrih.2024.02.001
中图分类号：
学科分类号：
摘要：
Background: Deep convolutional neural networks have garnered considerable attention in numerous machine learning applications, particularly in visual recognition tasks such as image and video analyses. There is a growing interest in applying this technology to diverse applications in medical image analysis. Automated three-dimensional Breast Ultrasound is a vital tool for detecting breast cancer, and computer-assisted diagnosis software, developed based on deep learning, can effectively assist radiologists in diagnosis. However, the network model is prone to overfitting during training, owing to challenges such as insufficient training data. This study attempts to solve the problem caused by small datasets and improve model detection performance. Methods: We propose a breast cancer detection framework based on deep learning (a transfer learning method based on cross-organ cancer detection) and a contrastive learning method based on breast imaging reporting and data systems (BI-RADS). Results: When using cross organ transfer learning and BIRADS based contrastive learning, the average sensitivity of the model increased by a maximum of 16.05%. Conclusion: Our experiments have demonstrated that the parameters and experiences of cross-organ cancer detection can be mutually referenced, and contrastive learning method based on BI-RADS can improve the detection performance of the model. © 2024 Beijing Zhongke Journal Publishing Co. Ltd
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页码：239 / 251
页数：12
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