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射线和视网膜成像，是有价值的工具，以协助诊断。使用深度学习模型，医学成像分析得到了显著的进步。从大量医疗数据中提取的知识可用于对新患者进行预测。在许多情况下，已经证明机器学习模型的性能与委员会认证的放射科医生或其他专业专家相当，这表明这些模型在临床应用中的潜在成功集成。例如，假设一个病人注意到他们皮肤上的无痛皮疹。如果他们能用手机拍下照片，并接受与经验丰富的皮肤科医生相当的快速评估，那么危及生命的疾病将得到早期干预或避免。然而，目前深度学习的成功在很大程度上依赖于大型和高质量的标记数据集。这种近乎完美的环境仅在理想的实验室环境中可用，因为在真实的临床应用中存在人口变化、设备差异或罕见疾病。该项目计划专注于非理想医学成像诊断环境的特定挑战，以推进构建可移植深度学习模型的知识，并通过提供更好的医学成像诊断工具来增强国民健康。此外，这项研究将支持跨学科的发展，一个不同的队列博士。在技术上，这个项目将研究和建立在不同环境中可转移的医学成像诊断系统。该项目的一个首要主题是利用深度神经网络的未充分研究的几何特性，以解决在各种环境中部署医学成像系统时的三个普遍障碍。具体来说，将模型转移到新的类和新的领域是一个挑战，在新的类中没有足够的训练样本，在新的领域中部署环境会发生变化，更重要的是，要保留模型中以前的知识。如果成功的话，这项研究有望通过利用深度神经网络的新几何解释将输入和特征空间划分为广义Voronoi图来促进对构建迁移深度学习模型的理解。所提出的技术的驱动应用是预测胸部X光片上的长尾疾病模式，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，所提出的方法有可能被扩展到类似的情况下，不同的部署环境。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.