使用EfficientNet进行前列腺活检格里森分级的深度学习网络。

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2023-04-25 DOI:10.1515/bmt-2022-0201

Karthik Ramamurthy, Abinash Reddy Varikuti, Bhavya Gupta, Nehal Aswani

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引用次数: 2

摘要

目的:肿瘤的严重程度分级是诊断的关键。格里森评分是一种广泛使用的前列腺癌分级系统。人工检查显微图像并给它们分级是令人厌烦的，而且耗费大量时间。因此，为了实现格里森分级过程的自动化，本文提出了一种新的深度学习网络。方法:在本工作中，提出了一个基于effentnet架构的前列腺癌Gleason分级深度学习网络。它采用复合缩放方法来平衡底层网络的维度。此外，在EfficientNet-B7中增加了一个额外的注意分支，用于精确的特征加权。结果:据我们所知，这是第一个将额外的注意力分支与用于Gleason分级的EfficientNet体系结构集成在一起的工作。所提出的模型使用哈佛Dataverse数据集中来自前列腺癌组织微阵列(tma)的h&e染色样本进行训练。结论:所提出的网络优于现有的方法，Kappa得分为0.5775。

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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A deep learning network for Gleason grading of prostate biopsies using EfficientNet.

Objectives: The most crucial part in the diagnosis of cancer is severity grading. Gleason's score is a widely used grading system for prostate cancer. Manual examination of the microscopic images and grading them is tiresome and consumes a lot of time. Hence to automate the Gleason grading process, a novel deep learning network is proposed in this work.

Methods: In this work, a deep learning network for Gleason grading of prostate cancer is proposed based on EfficientNet architecture. It applies a compound scaling method to balance the dimensions of the underlying network. Also, an additional attention branch is added to EfficientNet-B7 for precise feature weighting.

Result: To the best of our knowledge, this is the first work that integrates an additional attention branch with EfficientNet architecture for Gleason grading. The proposed models were trained using H&E-stained samples from prostate cancer Tissue Microarrays (TMAs) in the Harvard Dataverse dataset.

Conclusions: The proposed network was able to outperform the existing methods and it achieved an Kappa score of 0.5775.

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.