SCH: Leverage clinical knowledge to augment deep learning analysis of breast images

SCH：利用临床知识增强乳腺图像的深度学习分析

• 批准号: 10659235
• 负责人: Shandong Wu
• 金额: $ 28.35万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10659235
• 关键词: Address; Algorithms; Artificial Intelligence; Benign; Bilateral; Biological; Breast; Breast Cancer Detection; Breast Cancer Risk Assessment Tool; Breast Cancer Risk Factor; Classification; Clinical; Clinical Sciences; Computational Technique; Computer Assisted; Consumption; Data; Data Set; Detection; Development; Devices; Diagnosis; Disease; Early Diagnosis; Engineering; Generations; Goals; Grain; Hybrids; Image; Image Analysis; Intervention; Knowledge; Label; Learning; Lesion; Literature; Malignant Neoplasms; Mammographic Density; Mammography; Maps; Medical; Medical Imaging; Methodology; Methods; Modeling; Organ; Patients; Pattern; Periodicals; Predisposition; Principal Investigator; Research; Research Personnel; Risk; Risk Assessment; Risk Factors; Risk Marker; Risk Reduction; Scheme; Source Code; Statistical Models; Structure; Technology; Texture; Time; Training; Triage; Woman; Work; biomedical imaging; breast cancer diagnosis; breast imaging; breast lesion; clinical application; clinical imaging; computer aided detection; deep learning; deep learning model; design; digital; experience; generative adversarial network; imaging modality; improved; innovation; insight; large datasets; malignant breast neoplasm; multidisciplinary; novel; novel strategies; programs; risk prediction; screening; serial imaging; spatiotemporal; success; tool; transfer learning; trustworthiness

Artificial intelligence (AI) technologies have achieved remarkable success in medical image-based applications. Today, there are unprecedented needs in developing novel strategies and methodologies to enable robust, trustworthy, and accessible AI for various applications. Classic deep learning training is driven purely by data. In the medical domain, clinical knowledge is often available and useful, but is mostly ignored in the current practice of AI research. Incorporating clinical knowledge into deep learning modeling requires an in-depth understanding of medical context/workflow. This calls for multi-disciplinary collaborative research using computational techniques and clinical sciences to advance the biomedical data/AI research. The overall goal of this project is to develop a new paradigm of deep learning that combines imaging data and clinical knowledge to augment breast cancer diagnosis, risk assessment, and lesion detection. We will develop technical innovations on breast imaging to address deep learning modeling on small datasets, longitudinal examinations, and content-efficient images, through three specific aims: Aim 1: Formulate auxiliary tasks/assessment into model training of CNNs for breast cancer diagnosis on small datasets; Aim 2: Employ biological relationships of images to guide deep learning structure design for breast cancer risk prediction using longitudinal data; Aim 3: Develop a knowledge-guided unsupervised pipeline for identification of a suspicion map to support deep learning analysis on content-efficient images. These aims represent novel applied methodological development to build roust deep learning models for important clinical imaging applications. We have strong preliminary results for each aim and an experienced research team covering computational, biomedical, engineering, and clinical sciences. Our proposed study has a broader impact on developing robust and innovative AI strategies/methods to enable clinical imaging AI applications. Going beyond breast imaging, our proposed concepts, paradigms, and methods can also be adapted/applicable to other diseases and imaging modalities, leading to benefits for a wide range of biomedical imaging analyses. Any algorithms, knowledge, insights, and experience gained from this study will have a direct and substantial impact on the rapid evolvement and applications of medical imaging AI devices, ultimately benefiting the researchers, clinicians, and patients.

人工智能（AI）技术在基于医学图像的领域取得了显着的成功 应用程序。今天，前所未有地需要开发新的战略和方法来 为各种应用程序提供强大、值得信赖且可访问的人工智能。经典的深度学习训练是 纯粹由数据驱动。在医学领域，临床知识通常是可用且有用的，但大多是 在目前的人工智能研究实践中被忽视。将临床知识融入深度学习建模中 需要深入了解医疗背景/工作流程。这需要多学科 使用计算技术和临床科学的合作研究来推进生物医学 数据/人工智能研究。该项目的总体目标是开发一种新的深度学习范式 结合影像数据和临床知识来增强乳腺癌诊断、风险评估和 病变检测。我们将开发乳腺成像技术创新来解决深度学习问题 通过三个特定的方法对小数据集、纵向检查和内容高效的图像进行建模 目标：目标 1：将辅助任务/评估纳入用于乳腺癌诊断的 CNN 模型训练中 在小数据集上；目标2：利用图像的生物学关系来指导深度学习结构设计 使用纵向数据进行乳腺癌风险预测；目标 3：开发知识引导的无监督 用于识别怀疑图以支持对内容高效图像进行深度学习分析的管道。 这些目标代表了新的应用方法论的发展，旨在为以下领域建立鲁棒的深度学习模型： 重要的临床成像应用。我们对每个目标都有强有力的初步结果，并且 经验丰富的研究团队涵盖计算、生物医学、工程和临床科学。我们的 拟议的研究对开发稳健和创新的人工智能战略/方法具有更广泛的影响，以实现 临床影像人工智能应用。超越乳腺成像，我们提出的概念、范式和 方法还可以适应/适用于其他疾病和成像方式，从而为患者带来好处 广泛的生物医学成像分析。获得的任何算法、知识、见解和经验 这项研究将对信息技术的快速发展和应用产生直接而实质性的影响。 医学成像人工智能设备，最终使研究人员、临床医生和患者受益。