M-ISIC: A Multimodal Open-Source International Skin Imaging Collaboration Informatics Platform for Automated Skin Cancer Detection

M-ISIC：用于自动皮肤癌检测的多模式开源国际皮肤成像协作信息学平台

• 批准号: 10528944
• 负责人: Kivanc Kose
• 金额: $ 90.65万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10528944
• 关键词: Address; Algorithms; Archives; Artificial Intelligence; Automation; Boston; Cancer Detection; Clinical; Clinical Distribution; Clinical Trials; Collaborations; Complex; Computer Vision Systems; Computer software; Custom; Data; Data Set; Data Sources; Database Management Systems; Dermatology; Detection; Development; Devices; Diagnosis; Diagnostic; Early Diagnosis; Education; Evaluation; Goals; Health; Human; Image; Informatics; Ingestion; International; Intervention; Label; Lead; Link; Machine Learning; Malignant Neoplasms; Manuals; Medical Education; Metadata; Methods; Modality; Modeling; Morbidity - disease rate; Morphologic artifacts; Multimodal Imaging; Online Systems; Operative Surgical Procedures; Organizational Efficiency; Participant; Pathology; Performance; Persons; Primary Health Care; Process; Quality Control; Radiology Specialty; Research; Resolution; Retrieval; Skin; Skin Cancer; Sorting - Cell Movement; Tattooing; Techniques; Technology; Testing; Training and Education; United States; Visual; Visualization; Work; algorithm development; analysis pipeline; archive data; archived data; artificial intelligence algorithm; base; cancer type; clinical application; clinical translation; data curation; data ingestion; data integration; data quality; data wrangling; design; diagnostic platform; digital; flexibility; image archival system; image registration; improved; informatics tool; large datasets; melanoma; microscopic imaging; mortality; multidimensional data; multimodal data; multimodality; next generation; open source; open source tool; prevent; quality assurance; reflectance confocal microscopy; research clinical testing; response; success; teledermatology; tool

ABSTRACT Skin cancer is the most common type of cancer in the United States. It is critical to detect it early as skin cancers, especially melanoma, can be cured by surgery alone if detected early. As digital technology improves, skin cancer detection, and especially automated skin cancer detection, is increasingly being performed over images either in person or remotely via teledermatology. While artificial intelligence (AI) for skin cancer detection exceeds human performance on static images, algorithm performance on representative, multimodal data is still underdeveloped due to data collected piecemeal with different devices, without consistent image acquisition standards or automated registration. A well-curated dataset of annotated skin images helps meet a unique need beyond machine learning, as primary care clinicians also require expertly annotated images for education and training. We will overcome the lack of imaging standards and disparate data sources problematic in dermatology imaging by developing automated ingestion, organization, registration, and curation pipeline to improve AI for skin cancer detection. The International Skin Imaging Collaboration (ISIC) Archive includes over 2,500 citations, 156,000 images, 100 daily users, and 5 AI grand challenges with over 3,500 participants. The ISIC archive is built upon the open- source, NCI- supported, open-source web-based data management platform, Girder. The Girder platform is highly flexible, and has been extended to multiple applications (e.g., pathology, radiology). The flexibility of the Girder platform will enable us to address four major barriers that prevent our ability to efficiently ingest, host and serve large amounts of multidimensional data at the scale of non-medical image repositories (e.g. ImageNet): (1) need for laborious expert data curation and quality assurance review for protected health information, imaging artifacts, and incorrect labels (SA1.1); (2) limited metadata without content-based features creating cumbersome image retrieval (SA1.2); (3) lack of multimodal viewing capabilities (SA2); and (4) inadequate integration to existing AI and annotation software, preventing flexible, hypothesis-driven experimentation (SA3). The proposed informatics project aimed at data ingestion, multimodal visualization, and organization through ML and computer vision-based automation build on the initial success of the International Skin Imaging Collaboration (ISIC) Archive and the Girder platform upon which it is built. They will enable scaling of the Archive to millions of images, enabling multimodal experimentation with registered reflectance confocal microscopy images, and nimbly facilitate AI and translational experimentation for improved skin cancer detection.

摘要 皮肤癌是美国最常见的癌症类型。及早发现皮肤是非常关键的。 癌症，特别是黑色素瘤，如果及早发现，只需手术就可以治愈。随着数字技术的进步， 皮肤癌检测，特别是自动化皮肤癌检测，越来越多地被 您可以亲自或通过远程皮肤科远程获取图像。而皮肤癌的人工智能(AI) 检测在静态图像上超过人的性能，在代表性、多模式上的算法性能 由于使用不同的设备零散地收集数据，没有一致的图像，因此数据仍然不发达 采购标准或自动注册。经过精心整理的带注释的皮肤图像数据集有助于满足 除了机器学习之外的独特需求，因为初级保健临床医生还需要经过专业注释的图像 教育和培训。我们将克服缺乏成像标准和不同数据源的问题 通过开发自动摄取、组织、注册和管理在皮肤科成像中存在问题 改进皮肤癌检测人工智能的管道。 国际皮肤成像合作(ISIC)档案包括2500多条引文，15.6万张图片，100张 每日用户，5个AI盛大挑战，超过3500人参与。ISIC档案建立在开放的- 源代码，NCI支持，开源的基于Web的数据管理平台，BELDER。主梁平台为 高度灵活，并已扩展到多种应用(例如，病理学、放射学)。 大梁平台的灵活性将使我们能够解决阻碍我们能力的四个主要障碍 高效地摄取、托管和服务非医学图像规模的海量多维数据 存储库(如ImageNet)：(1)需要费力的专家数据管理和质量保证审查 受保护的健康信息、成像伪像和不正确的标签(SA1.1)；(2)元数据有限 基于内容的功能造成了繁琐的图像检索(SA1.2)；(3)缺乏多模式观看 功能(SA2)；以及(4)与现有人工智能和注释软件的集成不充分，无法灵活、 假设驱动实验(SA3)。 拟议的信息学项目旨在通过以下方式获取数据、多模式可视化和组织 ML和基于计算机视觉的自动化建立在国际皮肤成像的初步成功之上 合作(ISIC)档案馆及其构建的大梁平台。它们将支持扩展 归档到数百万张图像，支持使用配准的反射比共焦进行多模式实验 显微图像，并灵活地促进人工智能和翻译实验，以改善皮肤癌 侦测。