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基于病变的卷积神经网络在早期胃癌诊断中的应用

Lesion-Based Convolutional Neural Network in Diagnosis of Early Gastric Cancer.

作者信息

Yoon Hong Jin, Kim Jie-Hyun

机构信息

Division of Gastroenterology, Department of Internal Medicine, Soonchunhyang University College of Medicine, Cheonan, Korea.

Division of Gastroenterology, Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.

出版信息

Clin Endosc. 2020 Mar;53(2):127-131. doi: 10.5946/ce.2020.046. Epub 2020 Mar 30.

DOI:10.5946/ce.2020.046
PMID:32252505
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7137575/
Abstract

Diagnosis and evaluation of early gastric cancer (EGC) using endoscopic images is significantly important; however, it has some limitations. In several studies, the application of convolutional neural network (CNN) greatly enhanced the effectiveness of endoscopy. To maximize clinical usefulness, it is important to determine the optimal method of applying CNN for each organ and disease. Lesion�-based CNN is a type of deep learning model designed to learn the entire lesion from endoscopic images. This review describes the application of lesion-based CNN technology in diagnosis of EGC.

摘要

利用内镜图像诊断和评估早期胃癌（EGC）具有极其重要的意义；然而，它也存在一些局限性。在多项研究中，卷积神经网络（CNN）的应用极大地提高了内镜检查的有效性。为了最大限度地发挥临床实用性，确定针对每个器官和疾病应用CNN的最佳方法非常重要。基于病变的CNN是一种深度学习模型，旨在从内镜图像中学习整个病变。这篇综述描述了基于病变的CNN技术在早期胃癌诊断中的应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b10/7137575/9cda0911feb9/ce-2020-046f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b10/7137575/6e7bbd33d9a6/ce-2020-046f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b10/7137575/aedb25cbd55a/ce-2020-046f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b10/7137575/9cda0911feb9/ce-2020-046f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b10/7137575/6e7bbd33d9a6/ce-2020-046f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b10/7137575/aedb25cbd55a/ce-2020-046f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b10/7137575/9cda0911feb9/ce-2020-046f3.jpg

相似文献

[1]
Lesion-Based Convolutional Neural Network in Diagnosis of Early Gastric Cancer.

Clin Endosc. 2020-3

[2]
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[4]
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[5]
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[6]
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本文引用的文献

[1]
A Lesion-Based Convolutional Neural Network Improves Endoscopic Detection and Depth Prediction of Early Gastric Cancer.

J Clin Med. 2019-8-26

[2]
Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy.

Gastrointest Endosc. 2018-11-16

[3]
An Asian consensus on standards of diagnostic upper endoscopy for neoplasia.

Gut. 2018-11-12

[4]
Endoscopic Diagnostic Support System for cT1b Colorectal Cancer Using Deep Learning.

Oncology. 2018-8-21

[5]
Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks.

Gastrointest Endosc. 2018-8-16

[6]
Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy.

Gastroenterology. 2018-6-18

[7]
Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network.

PLoS One. 2018-1-19

[8]
Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images.

Gastric Cancer. 2018-1-15

[9]
Computer-Aided Diagnosis Based on Convolutional Neural Network System for Colorectal Polyp Classification: Preliminary Experience.

Oncology. 2017

[10]
Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs.

Radiology. 2017-11-2

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