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.

用于病理图像分析的可解释深度学习算法 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 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法医学的主观解释 组织病理学部分构成了临床决策和研究结果的基础。然而，它有 已经表明，对病理切片的这种主观解释受到大量观察者之间和 观察者内部的变异性。计算机视觉和深度学习的最新进展使得客观和 图像的自动分析。这些方法已成功应用于组织学图像 展示了发展客观图像解释范式的潜力。然而，显着 在使用这种对组织学图像进行客观分析之前，仍有待解决算法挑战 由临床医生和研究人员。利用开发和简化研究软件的丰富经验 基于深度学习，PI 将开创新颖的算法方法来应对这些挑战，包括 但不限于：（1）使用十亿像素大小的显微镜训练数据高效且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) 数据的根本性重新设计 用于整合来自显微镜图像和分子概况（来自多组学）的信息的融合范例 数据）以改进诊断和预后确定（3）开发可视化和解释 为研究人员和临床工作流程提供的软件，可提高临床和研究的验证性和可重复性。 该系统将以模块化、用户友好的方式设计，并且是开源的，可通过 GitHub 作为通用即插即用模块，可适应各种临床和研究应用。 我们还将开发一个网络资源，其中包含针对各种器官、疾病状态和亚型的预训练模型 这些将附有详细的手册，以便研究人员可以将深度学习应用于他们的特定领域 研究问题。总体而言，该实验室的研究将从病理图像中产生具有重大影响的发现 分析及其软件将使许多其他 NIH 资助的实验室能够在不同的领域做同样的事情 生物医学学科。