{"title":"用于数字病理学中稳健组织分割和分类的随机学习分层算法","authors":"","doi":"10.1016/j.ins.2024.121358","DOIUrl":null,"url":null,"abstract":"<div><p>Highly detailed and accurate segmentation and classification of images constitutes an important class of tasks in computer vision. Typical “universal” domain-agnostic methods are known to suffer from instabilities and are prone to adversarial perturbations. Natural heterogeneity inherent in biological tissue structures complicates the interpretation of images even by trained physicians. Yet, algorithms in the medical domain require a high level of stability and interpretability to ensure their adoption by clinical experts and acceptance in clinical decision-making. In this work, we propose a novel method for segmentation and classification to address these challenges. The method is based on a hierarchical approach and biologically-informed feature extraction. The method's technical pipeline includes the automatic extraction of key biologically-informed features typically considered by physicians. This is followed by image classification using these features. Both stages rely on randomized ML techniques. The proposed hierarchical biomedically-informed approach significantly improved the image classification quality compared to the baseline solution of image classification in the task of colorectal cancer (CRC) analysis. The average F1-score for the four tissue types increased from 0.737 to 0.956. Using tumor tissue classification task as an example, we showed that the proposed algorithm offers an effective and practical avenue to solve these challenging issues.</p></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":null,"pages":null},"PeriodicalIF":8.1000,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A hierarchical algorithm with randomized learning for robust tissue segmentation and classification in digital pathology\",\"authors\":\"\",\"doi\":\"10.1016/j.ins.2024.121358\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Highly detailed and accurate segmentation and classification of images constitutes an important class of tasks in computer vision. Typical “universal” domain-agnostic methods are known to suffer from instabilities and are prone to adversarial perturbations. Natural heterogeneity inherent in biological tissue structures complicates the interpretation of images even by trained physicians. Yet, algorithms in the medical domain require a high level of stability and interpretability to ensure their adoption by clinical experts and acceptance in clinical decision-making. In this work, we propose a novel method for segmentation and classification to address these challenges. The method is based on a hierarchical approach and biologically-informed feature extraction. The method's technical pipeline includes the automatic extraction of key biologically-informed features typically considered by physicians. This is followed by image classification using these features. Both stages rely on randomized ML techniques. The proposed hierarchical biomedically-informed approach significantly improved the image classification quality compared to the baseline solution of image classification in the task of colorectal cancer (CRC) analysis. The average F1-score for the four tissue types increased from 0.737 to 0.956. Using tumor tissue classification task as an example, we showed that the proposed algorithm offers an effective and practical avenue to solve these challenging issues.</p></div>\",\"PeriodicalId\":51063,\"journal\":{\"name\":\"Information Sciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.1000,\"publicationDate\":\"2024-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Sciences\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0020025524012726\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"N/A\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Sciences","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0020025524012726","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"N/A","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
摘要
高精细、高精度的图像分割和分类是计算机视觉领域的一类重要任务。众所周知,典型的 "通用 "领域识别方法存在不稳定性,容易受到对抗性扰动的影响。生物组织结构中固有的天然异质性使得即使是训练有素的医生也很难解读图像。然而,医学领域的算法需要高度的稳定性和可解释性,以确保其被临床专家采用并在临床决策中得到认可。在这项工作中,我们提出了一种新的分割和分类方法来应对这些挑战。该方法基于分层方法和生物特征提取。该方法的技术流程包括自动提取医生通常会考虑的关键生物特征。然后利用这些特征进行图像分类。这两个阶段都依赖于随机化的 ML 技术。在结直肠癌(CRC)分析任务中,与图像分类基线解决方案相比,所提出的分层生物信息方法显著提高了图像分类质量。四种组织类型的平均 F1 分数从 0.737 提高到 0.956。以肿瘤组织分类任务为例,我们表明所提出的算法为解决这些具有挑战性的问题提供了一种有效而实用的途径。
A hierarchical algorithm with randomized learning for robust tissue segmentation and classification in digital pathology
Highly detailed and accurate segmentation and classification of images constitutes an important class of tasks in computer vision. Typical “universal” domain-agnostic methods are known to suffer from instabilities and are prone to adversarial perturbations. Natural heterogeneity inherent in biological tissue structures complicates the interpretation of images even by trained physicians. Yet, algorithms in the medical domain require a high level of stability and interpretability to ensure their adoption by clinical experts and acceptance in clinical decision-making. In this work, we propose a novel method for segmentation and classification to address these challenges. The method is based on a hierarchical approach and biologically-informed feature extraction. The method's technical pipeline includes the automatic extraction of key biologically-informed features typically considered by physicians. This is followed by image classification using these features. Both stages rely on randomized ML techniques. The proposed hierarchical biomedically-informed approach significantly improved the image classification quality compared to the baseline solution of image classification in the task of colorectal cancer (CRC) analysis. The average F1-score for the four tissue types increased from 0.737 to 0.956. Using tumor tissue classification task as an example, we showed that the proposed algorithm offers an effective and practical avenue to solve these challenging issues.
期刊介绍:
Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions.
Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.