3D Inception U‐net with Asymmetric Loss for Cancer Detection in Automated Breast Ultrasound-Reference-Cited by-同舟云学术

3D Inception U‐net with Asymmetric Loss for Cancer Detection in Automated Breast Ultrasound

Published:2020-10-13 Issue:11 Volume:47 Page:5582-5591
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Med. Phys.

Author:

Wang Yi¹,Qin Chenchen¹,Lin Chuanlu¹,Lin Di²,Xu Min³,Luo Xiao³,Wang Tianfu¹,Li Anhua³,Ni Dong¹

Affiliation:

1. National‐Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen518060 China

2. The College of Intelligence and Computing Tianjin University Tianjin300354 China

3. Department of Ultrasound Sun Yat‐Sen University Cancer Center State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangzhou510060 China

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.14389

Reference32 articles.

1. Cancer statistics, 2019

2. Global cancer statistics

3. Comparison of the Performance of Screening Mammography, Physical Examination, and Breast US and Evaluation of Factors that Influence Them: An Analysis of 27,825 Patient Evaluations

4. Mammographic Density and the Risk and Detection of Breast Cancer

5. Combined Screening With Ultrasound and Mammography vs Mammography Alone in Women at Elevated Risk of Breast Cancer

Cited by 20 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Contextual embedding learning to enhance 2D networks for volumetric image segmentation;Expert Systems with Applications;2024-11

2. Edge of discovery: Enhancing breast tumor MRI analysis with boundary-driven deep learning;Biomedical Signal Processing and Control;2024-09

3. Mass detection in automated three dimensional breast ultrasound using cascaded convolutional neural networks;Physica Medica;2024-08

4. MpMsCFMA-Net: Multi-path Multi-scale Context Feature Mixup and Aggregation Network for medical image segmentation;Engineering Applications of Artificial Intelligence;2024-07

5. Exploring Kinetic Curves Features for the Classification of Benign and Malignant Breast Lesions in DCE-MRI;2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS);2024-06-26