TRIPODS+X:RES:Collaborative Research: Learning with Expert-In-The-Loop for Multimodal Weakly Labeled Data and an Application to Massive Scale Medical Imaging

在这个项目中开发的方法解决了机器学习和生命科学应用程序之间的紧迫问题。这项工作希望刺激丰富的后续研究，这些研究不仅建立在开发的数学工具的基础上，而且建立在该项目对可解释和交互式机器学习的关注中获得的见解上。作为一项关键应用，医学图像研究成果的实现将使报告的自动生成成为可能。这项技术可以显著减少放射科医生的工作量，从而减少人为差错，优化资源利用。归根结底，减少放射学中的人为错误或丢失病例可以极大地有利于患者的福祉和护理。更广泛地说，这项工作将促进医疗保健领域正在采用的数据驱动思维，从而帮助加速新的发现。该项目还影响教育，并涉及学生在不同学术阶段的智力和专业发展。机器学习在医学成像(和其他以人为中心的任务)中的大多数应用都集中在监督学习上，这需要大量昂贵的标签数据。这一限制在整个应用程序中反复出现，而在现实世界中使用机器学习需要健壮性以及在有限监督下工作的能力。这个项目的重点是开发新的机器学习工具，用于在弱监督下工作，同时促进可解释和交互学习的最新水平。此外，只要可行，就会注意大规模数据和纳入领域知识。该项目还将把它将取得的理论进步应用到现实世界的医学成像应用中。拟议工作的理论进展将依赖于几何学的工具，特别是度量学习(包括在无限维空间上)，由最优运输理论驱动的数学模型，以及基于神经网络和核心方法的非线性表示。该奖项反映了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。

The methods developed in this project address pressing problems at the interface of machine learning and a life-sciences application. This work hopes to spur a rich variety of followup research that not only builds on the mathematical tools developed but also on the insights gained from the project's focus on interpretable and interactive machine learning. As a key application, implementation of the technology obtained from this research on medical images will enable automatic report generation. This technology could reduce the workload of radiologists significantly, and hence reduce human error and optimize resource utilization. Ultimately, reduction in human error or missing cases in radiology can greatly benefit patient well-being and care. More broadly, the work will enhance the ongoing adoption of data-driven thinking in healthcare and thereby help accelerate new discoveries. This project also impacts education, and involves intellectual and professional development of students at a variety of academic stages. Most applications of machine learning to medical imaging (and other human-centric tasks) focus on supervised learning, which demands a large amount of expensive labeled-data. This limitation recurs throughout applications, while real-world use of machine learning demands robustness as well as an ability to work with limited supervision. This project focuses on developing new machine learning tools for working with weak-supervision, while advancing state-of-the-art in interpretable and interactive learning. Moreover, attention to large-scale data and incorporation of domain knowledge is paid, whenever feasible. The project shall also apply the theoretical advances that it will make to a real-world medical imaging application. Theoretical advances of the proposed work will rely on tools from geometry, especially metric learning (including over infinite dimensional spaces), mathematical models motivated by optimal transport theory, as well as nonlinear representations based on neural networks as well as kernel methods.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.