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A systematic review on application of deep learning in digestive system image processing.

作者信息

Zhuang Huangming, Zhang Jixiang, Liao Fei

机构信息

Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, 430060 Hubei China.

出版信息

Vis Comput. 2023;39(6):2207-2222. doi: 10.1007/s00371-021-02322-z. Epub 2021 Oct 31.


DOI:10.1007/s00371-021-02322-z
PMID:34744231
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8557108/
Abstract

With the advent of the big data era, the application of artificial intelligence represented by deep learning in medicine has become a hot topic In gastroenterology, deep learning has accomplished remarkable accomplishments in endoscopy, imageology, and pathology. Artificial intelligence has been applied to benign gastrointestinal tract lesions, early cancer, tumors, inflammatory bowel diseases, livers, pancreas, and other diseases. Computer-aided diagnosis significantly improve diagnostic accuracy and reduce physicians' workload and provide a shred of evidence for clinical diagnosis and treatment. In the near future, artificial intelligence will have high application value in the field of medicine. This paper mainly summarizes the latest research on artificial intelligence in diagnosing and treating digestive system diseases and discussing artificial intelligence's future in digestive system diseases. We sincerely hope that our work can become a stepping stone for gastroenterologists and computer experts in artificial intelligence research and facilitate the application and development of computer-aided image processing technology in gastroenterology.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/56b1/8557108/30e84c461a6f/371_2021_2322_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/56b1/8557108/f21d32b1aaa4/371_2021_2322_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/56b1/8557108/30e84c461a6f/371_2021_2322_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/56b1/8557108/f21d32b1aaa4/371_2021_2322_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/56b1/8557108/30e84c461a6f/371_2021_2322_Fig2_HTML.jpg

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[1]
A systematic review on application of deep learning in digestive system image processing.

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[2]
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[3]
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[10]
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[4]
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[5]
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本文引用的文献

[1]
Weakly Supervised Deep Ordinal Cox Model for Survival Prediction From Whole-Slide Pathological Images.

IEEE Trans Med Imaging. 2021-12

[2]
Deep Learning Methods for Heart Sounds Classification: A Systematic Review.

Entropy (Basel). 2021-5-26

[3]
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Int J Comput Assist Radiol Surg. 2021-6

[4]
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Med Image Anal. 2021-7

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[6]
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Med Image Anal. 2021-7

[7]
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Eur Radiol. 2021-11

[8]
Artificial intelligence assists identifying malignant versus benign liver lesions using contrast-enhanced ultrasound.

J Gastroenterol Hepatol. 2021-10

[9]
Accurate and Feasible Deep Learning Based Semi-Automatic Segmentation in CT for Radiomics Analysis in Pancreatic Neuroendocrine Neoplasms.

IEEE J Biomed Health Inform. 2021-9

[10]
Precise whole liver automatic segmentation and quantification of PDFF and R2* on MR images.

Eur Radiol. 2021-10

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