CAREER: Deploying Transferable Medical Imaging Diagnosis System in Diverse Environments

职业：在不同环境中部署可转移的医学影像诊断系统

• 批准号: 2239537
• 负责人: Mingchen Gao
• 金额: $ 57.85万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239537&HistoricalAwards=false
• 关键词: CAREER; Deploying; Transferable; Medical; Imaging

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.

医学成像，例如计算机断层扫描 (CT)、磁共振成像 (MRI)、胸部 X 光检查和视网膜成像，是辅助诊断的宝贵工具。使用深度学习模型，医学成像分析已取得显着进步。从大量医疗数据中提取的知识可用于对新患者进行预测。许多案例已证明，机器学习模型的性能可与经过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的潜在成功集成。例如，假设一名患者注意到皮肤上出现无痛性皮疹。如果他们能用手机拍照并获得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，当前深度学习的成功在很大程度上依赖于大型且高质量的标记数据集。由于实际临床应用中的人群转移、设备差异或罕见疾病，这种近乎完美的环境只有在理想的实验室环境中才能实现。该项目计划重点关注非理想医学影像诊断环境的具体挑战，以提高构建可迁移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，这项研究将支持多元化博士群体的跨学科发展。从技术上讲，该项目将研究并建立在不同环境中可转移的医学影像诊断系统。该项目的总体主题是利用深度神经网络未充分研究的几何特性来解决在各种环境中部署医学成像系统时面临的三个普遍障碍。具体来说，将模型转移到没有足够训练样本的新类别和部署环境发生变化的新领域方面存在挑战，更重要的是，保留模型中的先前知识。如果成功，所提出的研究预计将通过利用深度神经网络的新颖几何解释将输入和特征空间划分为广义 Voronoi 图来增进对构建迁移深度学习模型的理解。所提出技术的推动应用是预测胸部 X 光的长尾疾病模式，并确保服务不足的社区为青光眼提供一致的筛查服务。此外，所提出的方法有可能扩展到具有不同部署环境的类似场景。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。