TA-Net: Triple attention network for medical image segmentation

Yang Li; Jun Yang; Jiajia Ni; Ahmed Elazab; Jianhuang Wu

doi:10.1016/j.compbiomed.2021.104836

ScienceGate Book Chapters

JOURNAL ARTICLE

TA-Net: Triple attention network for medical image segmentation

Yang Li Jun Yang Jiajia Ni Ahmed Elazab Jianhuang Wu

Year: 2021 Journal: Computers in Biology and Medicine Vol: 137 Pages: 104836-104836 Publisher: Elsevier BV

DOI: 10.1016/j.compbiomed.2021.104836

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Artificial intelligence Segmentation Robustness (evolution) Convolutional neural network Encoder Pixel Feature (linguistics) Pattern recognition (psychology) Redundancy (engineering) Block (permutation group theory) Computer vision Mathematics

Metrics

Cited By

6.96

FWCI (Field Weighted Citation Impact)

Refs

0.97

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Retinal Imaging and Analysis

Health Sciences → Medicine → Radiology, Nuclear Medicine and Imaging

Digital Imaging for Blood Diseases

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

AI in cancer detection

Physical Sciences → Computer Science → Artificial Intelligence

TA-Net: Triple attention network for medical image segmentation

Metrics

Citation History

Topics

Related Documents

Eff-TA-Net: A Lightweight Model with Triple Attention for Medical Image Segmentation

TANet: Triple Attention Network for medical image segmentation

MEA-Net: multilayer edge attention network for medical image segmentation

SA-Net: A scale-attention network for medical image segmentation

MFA-Net: Multiscale Feature Attention Network for Medical Image Segmentation