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

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

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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.

Breast ultrasoundAutomated 3D breast ultrasoundBreast cancersDeep learningTransfer learningConvolutional neural networksComputer-aided diagnosisCross organ learning

Lingyun BAO、Zhengrui HUANG、Zehui LIN、Yue SUN、Hui CHEN、You LI、Zhang LI、Xiaochen YUAN、Lin XU、Tao TAN

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Affiliated Hangzhou First People's Hospital,School of Medicine,Westlake University,Hangzhou 330100,China

Faculty of Applied Sciences,Macao Polytechnic University,Macao,999078,China

Pathology Department,Changsha First Hospital,Changsha 410073,China

Radiology Department,Changsha First Hospital,Changsha 410073,China

College of Aerospace Science and Engineering,National University of Defense Technology,Changsha 410073,China

Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation,Changsha 410073,China

School of Informa-tion Science and Technology,ShanghaiTech University,Shanghai 201210,China

Faculty of Applied Sciences,Macao Polytechnic University,Macao 999078,China

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2024

10.1016/j.vrih.2024.02.001

虚拟现实与智能硬件(中英文)

虚拟现实与智能硬件(中英文)

ISSN：
年,卷(期)：2024.6(3)