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Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy

Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy

This lesson explores three gastrointestinal imaging techniques: radionuclide testing, colonic transit studies, and virtual colonoscopy.
Radionuclide Testing
Radionuclide testing is a sophisticated medical technique for assessing gastrointestinal motility. It focuses on gastric emptying and colonic transit time. Radioactive markers track the movement of food through the digestive system, providing insights into gastrointestinal disorders.
In gastric emptying studies, a meal's liquid and...

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Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

使用卷积神经网络进行深度学习图像分类

Joaquim Carreras¹

¹Department of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, Japan.

Journal of imaging

|August 28, 2024

概括

此摘要是机器生成的。

一个卷积神经网络 (CNN) 从组织学图像准确地分类乳病 (CD) 和其他十二指肠疾病. 这种人工智能 (AI) 模型在诊断胃肠道疾病方面表现出很高的性能.

关键词:

人工智能的人工智能是人工智能.这种癌症是癌症癌症.患有乳性疾病的患者.计算机视觉计算机视觉卷积神经网络是一种卷积神经网络.十二指甲的十二指甲.这是一种炎症炎症炎症炎症.炎症性肠病是一种炎症性肠病.机器学习是机器学习.转移学习转移学习

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科学领域:

胃肠病学胃肠病学
计算病理学计算病理学
人工智能在医学中的应用

背景情况:

性疾病 (CD) 是一种由引发的免疫媒介性肠病.
十二指肠活检的精确组织学分类对于诊断CD和其他疾病至关重要.
当前的诊断方法可能耗时,需要专门的专业知识.

研究的目的:

开发和评估一个卷积神经网络 (CNN) 用于分类乳病的组织图像.
评估CNN在区分CD与正常小肠和非特异性十二指肠炎症方面的表现.
调查CNN对其他胃肠道疾病 (包括腺癌和克罗恩病) 的分类能力.

主要方法:

一个CNN被训练在一个大数据集的血素和素 (H&E) 染色的十二指肠组织学图像.
该网络最初被训练分为三个类别:CD,正常小肠和非特异性十二指肠炎.
随后,CNN被重新训练,包括十二指甲状腺癌和克罗恩病,使用准确性,精度,回忆,F1得分和特异性等指标评估性能. 为了解释性,使用梯度加权类激活映射 (Grad-CAM).

主要成果:

美国有线电视新闻网在CD分类方面取得了高绩效,准确度,精度,回忆率,F1分数和特异性高于99%.
当呈现腺癌图像时,网络最初将其错误分类为炎症或正常,但重新训练改善了CD和腺癌分类,分别达到99%和97%以上.
该模型成功地结合了克罗恩病图像,并在五种不同的诊断中表现出高性能,Grad-CAM为分类决策提供了洞察力.

结论:

基于CNN的深度神经系统可以有效地从组织学图像中高精度地分类多种胃肠道诊断.
这种人工智能方法显示出增强胃肠病理学诊断能力的前景.
该研究强调了狭义人工智能 (AI) 在医学图像分析中的特定,高性能任务的潜力.