A Regional-Attentive Multi-Task Learning Framework for Breast Ultrasound Image Segmentation and Classification
Author:
Affiliation:
1. Department of Computer Science, Utah State University, Logan, UT, USA
2. Department of Computer Science and Technology, Kean University, Union, NJ, USA
Funder
Graduate Research and Creative Opportunity (GRCO) Grant of Utah State University
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10016712.pdf?arnumber=10016712
Reference42 articles.
1. Densely Connected Convolutional Networks
2. Computerized analysis of shadowing on breast ultrasound for improved lesion detection
3. Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don't Forget the Peritumoral Region
4. Multi-Task Learning with Context-Oriented Self-Attention for Breast Ultrasound Image Classification and Segmentation
5. A Multi-Task Learning Framework for Automated Segmentation and Classification of Breast Tumors From Ultrasound Images
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