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.

抽象的 皮肤癌是美国最常见的癌症类型。尽早发现皮肤病很重要 癌症，尤其是黑色素瘤，如果及早发现，可以仅通过手术治愈。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，越来越多地进行 亲自或通过远程皮肤病学远程提供图像。人工智能（AI）治疗皮肤癌 检测超出人类在静态图像上的表现，算法在代表性、多模态上的表现 由于数据是通过不同设备收集的，数据仍不完善，没有一致的图像 获取标准或自动注册。精心策划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，还有独特的需求，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、注册和管理来解决皮肤科成像中的问题 改进人工智能用于皮肤癌检测的管道。 国际皮肤成像合作组织 (ISIC) 档案包含超过 2,500 条引用、156,000 张图像、100 每日用户数，以及 5 个人工智能大挑战，超过 3,500 名参与者。 ISIC 档案建立在开放的基础上 源、NCI 支持、基于网络的开源数据管理平台 Girder。 Girder平台是 高度灵活，并已扩展到多种应用（例如病理学、放射学）。 Girder 平台的灵活性将使我们能够解决阻碍我们能力的四大障碍 以非医学图像的规模有效地摄取、托管和服务大量多维数据 存储库（例如 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、成像伪影和不正确的标签 (SA1.1)； (2) 元数据有限，无 基于内容的特征造成繁琐的图像检索（SA1.2）； (3)缺乏多模态观看 能力（SA2）； (4) 与现有人工智能和注释软件的集成不足，阻碍了灵活、 假设驱动的实验（SA3）。 拟议的信息学项目旨在通过数据摄取、多模式可视化和组织 基于机器学习和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 Collaboration (ISIC) Archive 及其构建基础的 Girder 平台。他们将能够扩展 存档数百万张图像，通过配准反射共焦实现多模态实验 显微图像，灵活地促进人工智能和转化实验以改善皮肤癌 检测。