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

人工智能(AI)技术在基于医学图像的应用中取得了显著的成功 申请。今天，人们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、值得信赖和可访问的人工智能。经典的深度学习培训是 纯粹由数据驱动。在医学领域，临床知识通常是可用的和有用的，但主要是 在当前的人工智能研究实践中被忽视了。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。这需要多学科的合作 利用计算技术和临床科学进行协作研究，以推动生物医学 数据/人工智能研究。这个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，增强乳腺癌的诊断、风险评估和 损伤检测。我们将开发乳房成像的技术创新，以应对深度学习 在小型数据集、纵向检查和内容高效型图像上进行建模，通过三个特定的 目的：目的1：将辅助任务/评估纳入用于乳腺癌诊断的神经网络模型训练 在小数据集上；目标2：利用图像的生物关系来指导深度学习结构设计 用于使用纵向数据预测乳腺癌风险；目标3：开发知识引导的无监督 用于识别可疑地图的管道，以支持对内容高效的图像进行深度学习分析。 这些目标代表了构建Roust深度学习模型的新的应用方法论发展 重要的临床影像应用。我们对每个目标都有很强的初步结果， 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 拟议的研究对开发强大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。除了乳房成像，我们提出的概念、范例和 这些方法也可以适应/适用于其他疾病和成像方式，从而为 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 这项研究将对生物多样性的快速演变和应用产生直接和实质性的影响 医学成像AI设备，最终使研究人员、临床医生和患者受益。

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.