CRII: CNS: OCEAN: A Once-for-All Edge Collaboration System for Medical Imaging

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Given the ability to learn complex representations in a data-driven manner, Deep learning algorithms have greatly impacted the medical imaging field. However, achieving reliable healthcare artificial intelligence (AI) technology is challenging due to the scarce annotation problem caused by the cost of professional labeling, heterogeneity of human labels, and concerns over healthcare information privacy. To overcome this scarce annotation problem, there are currently two solutions. One solution is enabling multi-institutional collaboration to train a dedicated model. Another is obtaining multi-scale features from training a shareable model to fine-tuning multiple tasks. The first method involves huge computation and communication costs to train a dedicated model for only one type of task; while the second method is currently only applied to the autonomous car where a rich amount of data is collected and processed frequently on a single node. This project seeks to fill this gap by innovatively training multiple medical imaging tasks together with a cache mechanism to build an efficient and effective multi-institutional collaborative system. The goal of the proposed research is to design a multi-task learning model for medical imaging tasks, design a cache mechanism for the system to active relevant portion when interpreting a specific task, and develop a prototype distributed multi-task learning system for medical imaging to evaluate the model and system performance of the proposed solutions. Building a one-for-all collaboration system for medical imaging will constitute a significant technological breakthrough toward achieving practical AI in clinical practice. By facilitating the model sharing within and among institutions, the proposed system can address the scarce annotation problem and accelerates clinical detection, diagnosis, and treatment, to benefit healthcare professionals. Furthermore, as a general-purpose framework, the proposed system will also be deployed to other fields with similar application requirements, such as connected autonomous driving and other mobility systems on land, in the air, and at sea, where multiple nodes work as a unit for a series of tasks. From the education aspect, the proposed system will be developed as a basic experimentation platform for healthcare AI and can be easily transplanted to other scenarios, such as smart homes and smart transportation. The proposed system will be used for undergraduate and graduate education and research with the goal to inspire students' interests in edge intelligence, broaden participation in intelligent computing, networking, and systems, and enhance education diversity, inclusion, and equity.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.