MI-UNet: Multi-Inputs UNet Incorporating Brain Parcellation for Stroke Lesion Segmentation From T1-Weighted Magnetic Resonance Images-Reference-Cited by-同舟云学术

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MI-UNet: Multi-Inputs UNet Incorporating Brain Parcellation for Stroke Lesion Segmentation From T1-Weighted Magnetic Resonance Images

Published:2021-02 Issue:2 Volume:25 Page:526-535
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Zhang Yue^ORCID,Wu Jiong^ORCID,Liu Yilong^ORCID,Chen Yifan^ORCID,Wu Ed X.^ORCID,Tang Xiaoying^ORCID

Funder

Shenzhen Basic Research Program

National Key R&D Program of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Biotechnology

Link

http://xplorestaging.ieee.org/ielx7/6221020/9348958/09099078.pdf?arnumber=9099078

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1. Unfolding Explainable AI for Brain Tumor Segmentation;Neurocomputing;2024-09

2. Segmentation of stroke lesions using transformers‐augmented MRI analysis;Human Brain Mapping;2024-08

3. DRRN: Differential rectification & refinement network for ischemic infarct segmentation;CAAI Transactions on Intelligence Technology;2024-07-24

4. AM-EEGNet: An advanced multi-input deep learning framework for classifying stroke patient EEG task states;Neurocomputing;2024-06

5. Deep learning models for ischemic stroke lesion segmentation in medical images: A survey;Computers in Biology and Medicine;2024-06

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