Interpretable Deep Learning Algorithms for Pathology Image Analysis

用于病理图像分析的可解释深度学习算法

• 批准号: 10256621
• 负责人: Faisal Mahmood
• 金额: $ 44.75万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10256621
• 关键词: Address; Algorithms; Artificial Intelligence; Biological Process; Biomedical Research; Classification; Clinical; Clinical Research; Computer Vision Systems; Computer software; Data; Development; Diagnosis; Diagnostic; Discipline; Disease; Forensic Medicine; Funding; Heart; Histology; Histopathology; Image; Image Analysis; Interobserver Variability; Intraobserver Variability; Label; Laboratories; Laboratory Research; Manuals; Methods; Microscopic; Modeling; Molecular Profiling; Multiomic Data; Organ; Outcomes Research; Pathology; Play; Prognosis; Research; Research Personnel; Slide; Supervision; System; Tissue Stains; Training; United States National Institutes of Health; Validation; Visualization; automated analysis; automated image analysis; base; cancer diagnosis; clinical decision-making; data fusion; decision research; deep learning; deep learning algorithm; design; disease diagnosis; experience; improved; intelligent algorithm; microscopic imaging; novel; online resource; open source; pathology imaging; predicting response; prognostic; success; treatment response; user-friendly

Interpretable Deep Learning Algorithms for Pathology Image Analysis Abstract The microscopic examination of stained tissue is a fundamental component of biomedical research and for the understanding of biological processes of disease which leads to improved diagnosis, prognosis and therapeutic response prediction. Ranging from cancer diagnosis to heart rejection and forensics the subjective interpretation of histopathology sections forms the basis of clinical decision making and research outcomes. However, it has been shown that such subjective interpretation of pathology slides suffers from large interobserver and intraobserver variability. Recent advances in computer vision and deep learning has enabled the objective and automated analysis of images. These methods have been applied with success to histology images which have demonstrated potential for development of objective image interpretation paradigms. However, significant algorithmic challenges remain to be addressed before such objective analysis of histology images can be used by clinicians and researchers. Leveraging extensive experience in developing and decimating research software based on deep learning the PI will pioneer novel algorithmic approaches to address these challenges including but not limited to: (1) training data-efficient and interpretable deep learning models with gigapixel size microscopy images for classification and segmentation using weakly supervised labels (2) fundamental redesign of data fusion paradigms for integrating information from microscopy images and molecular profiles (from multi-omics data) for improved diagnostic and prognostic determinations (3) developing visualization and interpretation software for researchers and clinical workflows to improve clinical and research validation and reproducability. The system will be designed in a modular, user-friendly manner and will be open-source, available through GitHub as universal plug-and-play modules ready to be adapted to various clinical and research applications. We will also develop a web resource with pretrained models for various organs, disease states and subtypes these will be accompanied with detailed manuals so researchers can apply deep learning to their specific research problems. Overall, the laboratory’s research will yield high impact discoveries from pathology image analysis, and its software will enable many other NIH funded laboratories to do the same, across various biomedical disciplines.

病理学图像分析的可解释的深度学习算法 抽象的 染色组织的显微镜检查是生物医学研究的基本组成部分，也是 了解疾病的生物学过程，从而改善诊断，预后和治疗 响应预测。从癌症诊断到心脏排斥和取证，主观解释 组织病理学部分构成了临床决策和研究结果的基础。但是，它有 结果表明，这种病理学的这种主观解释幻灯片遭受了大型观察者和 观察者内变异性。计算机视觉和深度学习的最新进展使目标和 图像的自动分析。这些方法已成功地应用于具有的组织学图像 具有开发客观图像解释范例的潜力。但是，很重要 在使用组织学图像的客观分析之前，算法挑战仍有待解决 由临床医生和研究人员。利用在开发和破坏研究软件方面的丰富经验 基于深度学习，PI将开创小说算法的方法，以应对这些挑战，包括 但不限于：（1）具有Gigapixel尺寸显微镜的培训数据效率和可解释的深度学习模型 使用弱监督标签进行分类和分割的图像（2）数据的基本重新设计 用于整合显微镜图像和分子曲线信息的融合范式（来自多摩变 数据）用于改进诊断和预后测定（3）开发可视化和解释 研究人员和临床工作流的软件，以改善临床和研究验证和繁殖。 该系统将以模块化，用户友好的方式设计，并将通过开源，可通过 Github作为通用插件模块，准备适应各种临床和研究应用。 我们还将为各种器官，疾病状态和亚型提供验证的模型开发网络资源 这些将伴随着详细的手册，以便研究人员可以将深度学习应用于其特定 研究问题。总体而言，实验室的研究将从病理图像中产生高影响的发现 分析及其软件将使许多其他NIH资助的实验室能够在各种 生物医学学科。