3D multi-view tumor detection in automated whole breast ultrasound using deep convolutional neural network

作者：

Highlights：

• A new feature extraction network is designed for breast ultrasound image detection.

• A 3D position analysis scheme is proposed to fuse 2D results and remove FPs.

• Quantitative and qualitative analysis are shown for 2D and 3D detection results.

• The proposed 3D detection method gets the highest sensitivity at 0.95 with 0.57 FPs.

摘要

•A new feature extraction network is designed for breast ultrasound image detection.•A 3D position analysis scheme is proposed to fuse 2D results and remove FPs.•Quantitative and qualitative analysis are shown for 2D and 3D detection results.•The proposed 3D detection method gets the highest sensitivity at 0.95 with 0.57 FPs.

论文关键词：Automated breast ultrasound (ABUS),3D detection,Multi-view detection,Deep learning,Majority voting,Candidates fusion

论文评审过程：Received 12 December 2019, Revised 6 November 2020, Accepted 27 November 2020, Available online 1 December 2020, Version of Record 5 December 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.114410