TS-Net: A Deep Learning Framework for Automated Assessment of Longitudinal Tumor Volume Changes in an Orthotopic Breast Cancer Model Using MRI

Author:

Jun Yunkyoung1,Jin Seokha1ORCID,Myung Noehyun1ORCID,Jeong Jiwoo1,Lee Jimin2,Cho Hyung Joon1ORCID

Affiliation:

1. Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Eonyang-eup, Ulsan, Ulju-gun, Republic of Korea

2. Department of Nuclear Engineering, Ulsan National Institute of Science and Technology (UNIST), Eonyang-eup, Ulsan, Ulju-gun, Republic of Korea

Funder

Korea Health Technology Research and Development Project through the Korea Health Industry Development Institute

Ministry of Health & Welfare, Republic of Korea

Institute of Information & Communications Technology Planning & Evaluation

the Ministry of Science and ICT (MSIT), Republic of Korea

Artificial Intelligence Graduate School Program

National Research Foundation of Korea

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Reference39 articles.

1. U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

2. A survey on Image Data Augmentation for Deep Learning

3. Diagnostic accuracy of MRI in predicting breast tumor size: Comparative analysis of MRI vs histopathological assessed breast tumor size;jethava;Conn Med,2015

4. Adam: A method for stochastic optimization;kingma;arXiv 1412 6980,2014

5. U-Net: Convolutional networks for biomedical image segmentation;ronneberger;Proc Int Conf Med Image Comput Comput -Assist Intervent,2015

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