SHA-MTL: soft and hard attention multi-task learning for automated breast cancer ultrasound image segmentation and classification-Reference-Cited by-同舟云学术

SHA-MTL: soft and hard attention multi-task learning for automated breast cancer ultrasound image segmentation and classification

Published:2021-07-12 Issue:10 Volume:16 Page:1719-1725
ISSN:1861-6410
Container-title:International Journal of Computer Assisted Radiology and Surgery
language:en
Short-container-title:Int J CARS

Author:

Zhang Guisheng,Zhao Kehui,Hong Yanfei,Qiu Xiaoyu,Zhang Kuixing,Wei Benzheng^ORCID

Funder

Natural Science Foundation of China

Introduction and Cultivation Program for Young Creative Talents in Colleges and Universities of Shandong Province

Natural Science Foundation of Shandong Province

Project of Science and technology plan of Shandong higher education institutions Program

Project of Shandong Province Medical and Health Technology Development Program

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Radiology Nuclear Medicine and imaging,General Medicine,Surgery,Computer Graphics and Computer-Aided Design,Computer Science Applications,Computer Vision and Pattern Recognition,Biomedical Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11548-021-02445-7.pdf

Reference13 articles.

1. Global Burden of Disease Cancer Collaboration (2015) The Global Burden of Cancer 2013. JAMA Oncol 1(4):505–527

2. Cheng Heng-Da, Shan Juan, Wen Ju, Guo Yanhui, Zhang Ling (2010) Automated breast cancer detection and classification using ultrasound images: a survey. Pattern Recognit 43(1):299–317

3. Xue C, Zhu L, Huazhu F, Xiaowei H, Li X, Zhang H, Heng P-A (2021) Global guidance network for breast lesion segmentation in ultrasound images. Med Image Anal 70:101989

4. Huang Q, Huang Y, Luo Y, Yuan F, Li X (2020) Segmentation of breast ultrasound image with semantic classification of superpixels. Med Image Anal 61:101657

5. Moon WK, Lee Y-W, Ke H-H, Lee SH, Huang C-S, Chang, R-F (2020) Computer-aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks. Computer Methods and Programs in Biomedicine, 190:105361

Cited by 48 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. A Novel Deep Learning Model for Breast Tumor Ultrasound Image Classification with Lesion Region Perception;Current Oncology;2024-08-28

2. High‐level feature‐guided attention optimized neural network for neonatal lateral ventricular dilatation prediction;Medical Physics;2024-08-27

3. An Interpretable Breast Ultrasound Image Classification Algorithm Based on Convolutional Neural Network and Transformer;Mathematics;2024-07-28

4. DBL-Net: A dual-branch learning network with information from spatial and frequency domains for tumor segmentation and classification in breast ultrasound image;Biomedical Signal Processing and Control;2024-07

5. AMTLUS: Attention-guided multi-task learning with uncertainty estimation in skin lesion segmentation and classification;Multimedia Tools and Applications;2024-05-20