Deep supervised learning using self-adaptive auxiliary loss for COVID-19 diagnosis from imbalanced CT images-Reference-Cited by-同舟云学术

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Deep supervised learning using self-adaptive auxiliary loss for COVID-19 diagnosis from imbalanced CT images

Published:2021-10 Issue: Volume:458 Page:232-245
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Hu Kai,Huang Yingjie,Huang Wei,Tan Hui,Chen Zhineng,Zhong Zheng,Li Xuanya,Zhang Yuan,Gao Xieping

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hunan Province

Scientific Research Foundation of Education Department of Anhui Province of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference49 articles.

1. Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for covid-19;Shi;IEEE Reviews in Biomedical Engineering,2020

2. Imaging and clinical features of patients with novel coronavirus sars-cov-2;Xu;European Journal of Nuclear Medicine and Molecular Imaging,2020

3. Automatic segmentation of retinal layer boundaries in oct images using multiscale convolutional neural network and graph search;Hu;Neurocomputing,2019

4. Automatic segmentation of intracerebral hemorrhage in ct images using encoder–decoder convolutional neural network;Hu;Information Processing & Management,2020

5. A systematic study of the class imbalance problem in convolutional neural networks;Buda;Neural Networks,2018

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