Multi-categorical classification using deep learning applied to the diagnosis of gastric cancer

Q4 Medicine
J. Kloeckner, Tatiana K. Sansonowicz, Á. L. Rodrigues, Tatiana W. N. Nunes
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引用次数: 2

Abstract

Introduction: Pathologists currently face a substantial increase in workload and complexity of their diagnosis work on different types of cancer. This is due to the increased incidence and detection of neoplasms, associated with diagnostic subspecialization and the advent of personalized medicine. There are numerous treatments available for different types of cancer, and the diagnosis must be dispensed quickly and accurately for each case. Deep learning is a tool that has been used in daily life, including image detection, and there is growing interest in its application in Medicine and especially in Pathology, where it has a revolutionary potential. Objective: In this article, we present deep learning, in particular convolutional neural networks, as a potential technique for the analysis of digitized images of histopathological slides, detecting identifiable patterns in an automated manner, introducing the possibility of applying this technology as an auxiliary tool in the diagnosis of neoplasms, especially in gastric cancer, the object of this preliminary study. Method: From a database of digitized images of histopathological slides representative of gastric cancer, we identified three morphological patterns of neoplasia, as well as non-neoplastic tissue patterns, with which we train a convolutional neural network algorithm, designed to identify and categorize similar images within these standards, in an automated manner. Results: The results of identification and automatic classification in the defined categories were satisfactory, with ROC curves above 0.9. Conclusion: The results show the potential application of convolutional neural networks for digitized slides of gastric cancer, in accordance with international literature findings.
基于深度学习的多类别分类在胃癌诊断中的应用
导读:病理学家目前面临着工作量的大幅增加和不同类型癌症的诊断工作的复杂性。这是由于肿瘤的发病率和检测的增加,与诊断的亚专业化和个性化医疗的出现有关。针对不同类型的癌症有许多治疗方法,必须对每个病例快速准确地进行诊断。深度学习是一种已经在日常生活中使用的工具,包括图像检测,人们对它在医学上的应用越来越感兴趣,尤其是在病理学上,它具有革命性的潜力。目的:在本文中,我们介绍了深度学习,特别是卷积神经网络,作为一种潜在的技术,用于分析组织病理切片的数字化图像,以自动化的方式检测可识别的模式,并介绍了将该技术作为辅助工具应用于肿瘤诊断的可能性,特别是在胃癌,这是本初步研究的对象。方法:从具有代表性的胃癌组织病理切片的数字化图像数据库中,我们确定了三种肿瘤的形态模式,以及非肿瘤组织的模式,我们用这些模式训练了一个卷积神经网络算法,旨在自动识别和分类这些标准内的相似图像。结果:在定义的分类中,识别和自动分类结果令人满意,ROC曲线均在0.9以上。结论:与国际文献研究结果一致,显示了卷积神经网络在胃癌数字化切片中的潜在应用前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Jornal Brasileiro de Patologia e Medicina Laboratorial
Jornal Brasileiro de Patologia e Medicina Laboratorial Health Professions-Medical Laboratory Technology
CiteScore
1.30
自引率
0.00%
发文量
0
审稿时长
20 weeks
期刊介绍: The Jornal Brasileiro de Patologia e Medicina Laboratorial (Brazilian Journal of Pathology and Laboratory Medicine), a continuation of Jornal Brasileiro de Patologia (Brazilian Journal of Pathology), and published quarterly (March, June, September and December) is directed towards the publication of scientific articles that contribute to the development of the area of Laboratory Medicine (Clinical Pathology, Pathology, Cytopathology). It accepts the following categories of articles: original articles, review articles, case reports, short communications, updating articles, letters to editors and reviews.
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