CAREER: Deploying Transferable Medical Imaging Diagnosis System in Diverse Environments

医学成像，如计算机断层扫描(CT)、磁共振成像(MRI)、胸部X光和视网膜成像，是辅助诊断的宝贵工具。使用深度学习模型，医学成像分析已经有了显著的进步。从大量医学数据中提取的知识可以用于对新患者进行预测。在许多情况下，机器学习模型的性能可以与委员会认证的放射科医生或其他专业专家相媲美，这表明这些模型在临床应用中具有潜在的成功整合潜力。例如，假设一个病人注意到他们的皮肤上出现了无痛性皮疹。如果他们能用手机拍照，并得到与经验丰富的皮肤科医生相当的快速评估，危及生命的疾病将被及早干预或避免。然而，目前深度学习的成功在很大程度上依赖于大量高质量的标记数据集。这种近乎完美的环境只有在理想的实验室环境中才能使用，因为人口迁移、设备差异或实际临床应用中的罕见疾病。本项目计划聚焦于非理想医学影像诊断环境的具体挑战，以提高构建可移植深度学习模型的知识，并通过为医学影像诊断提供更好的工具来增强国民健康。此外，这项研究将支持不同的博士和本科生群体的跨学科发展以及面向不同社区的外展活动。在技术上，该项目将在不同的环境中调查和建立可转移的医学影像诊断系统。该项目的一个主要主题是利用深度神经网络未被充分研究的几何特性，以解决在不同环境中部署医学成像系统时的三个普遍障碍。具体地说，在将模型转移到新类和新领域方面存在挑战，在新类中没有足够的训练样本，在新领域中部署环境发生变化，更重要的是，在模型中保留先前的知识。如果成功，这项研究有望通过利用深度神经网络的一种新的几何解释，将输入和特征空间划分为广义Voronoi图，来促进对建立转移深度学习模型的理解。拟议技术的主要应用是在胸部X光上预测长尾疾病模式，并确保在服务不足的社区提供一致的青光眼筛查服务。此外，建议的方法有可能扩展到具有不同部署环境的类似场景。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Medical imaging, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), chest X-ray, and retinal imaging, are valuable tools to assist in diagnosis. Medical imaging analysis has been significantly advanced using deep learning models. The knowledge extracted from large amounts of medical data can be used to make predictions for new patients. It has been demonstrated in many cases that the performances of machine learning models are comparable to board-certified radiologists or other professional experts, indicating the potential successful integration of those models in clinical applications. For example, imagine a patient notices a painless rash on their skin. If they could take a photo with a cell phone and receive a quick assessment comparable to experienced dermatologists, life-threatening diseases would be intervened or avoided early. However, the current success of deep learning is heavily dependent on large and high-quality labeled datasets. Such nearly perfect environments are only available in ideal lab environments because of the population shift, device differences, or rare diseases in real clinical applications. This project plans to focus on those specific challenges of non-ideal medical imaging diagnosis environments to advance the knowledge of building transferrable deep learning models and enhance national health by providing better tools for medical imaging diagnosis. Furthermore, this research will support the cross-disciplinary development of a diverse cohort of Ph.D. and undergraduate students and outreach activities to diverse communities.Technically, this project will investigate and build transferable medical imaging diagnosis systems in diverse environments. The project proceeds with one overarching theme of leveraging the understudied geometric properties of deep neural networks to address three universal barriers when deploying medical imaging systems in various environments. Specifically, there are challenges to transferring models to novel classes, where there are not enough training samples, and novel domains, where the deploying environments change, and more importantly, preserving the previous knowledge in the model. If successful, the proposed research is expected to advance the understanding of building transferring deep learning models by leveraging a novel geometric interpretation of deep neural networks partitioning the input and feature space into generalized Voronoi diagrams. The driving applications of the proposed techniques are the prediction of long-tailed disease patterns on chest X-rays and ensuring consistent screening services for glaucoma in underserved communities. In addition, the proposed methods have the potential to be extended to similar scenarios with diverse deployment environments.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.