{"title":"使用EfficientNet进行前列腺活检格里森分级的深度学习网络。","authors":"Karthik Ramamurthy, Abinash Reddy Varikuti, Bhavya Gupta, Nehal Aswani","doi":"10.1515/bmt-2022-0201","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Result: </strong>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.</p><p><strong>Conclusions: </strong>The proposed network was able to outperform the existing methods and it achieved an Kappa score of 0.5775.</p>","PeriodicalId":8900,"journal":{"name":"Biomedical Engineering / Biomedizinische Technik","volume":"68 2","pages":"187-198"},"PeriodicalIF":1.3000,"publicationDate":"2023-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A deep learning network for Gleason grading of prostate biopsies using EfficientNet.\",\"authors\":\"Karthik Ramamurthy, Abinash Reddy Varikuti, Bhavya Gupta, Nehal Aswani\",\"doi\":\"10.1515/bmt-2022-0201\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Result: </strong>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.</p><p><strong>Conclusions: </strong>The proposed network was able to outperform the existing methods and it achieved an Kappa score of 0.5775.</p>\",\"PeriodicalId\":8900,\"journal\":{\"name\":\"Biomedical Engineering / Biomedizinische Technik\",\"volume\":\"68 2\",\"pages\":\"187-198\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2023-04-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomedical Engineering / Biomedizinische Technik\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1515/bmt-2022-0201\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Engineering / Biomedizinische Technik","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1515/bmt-2022-0201","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
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.
期刊介绍：
Biomedical Engineering / Biomedizinische Technik (BMT) is a high-quality forum for the exchange of knowledge in the fields of biomedical engineering, medical information technology and biotechnology/bioengineering. As an established journal with a tradition of more than 60 years, BMT addresses engineers, natural scientists, and clinicians working in research, industry, or clinical practice.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
