Large-scale Deep Learning Algorithms for Video Analysis

The project's goal was to research and develop an innovative method to analyze medical video data, especially microcirculation videos, in a scalable manner within a clinical environment. This project is at the intersection between deep learning, image processing, early detection of diseases, system architecture, and clinical practice. This goal was attained by: · researching and developing a novel method in object detection and object classification to analyze microcirculation video data, and · researching and developing a software architecture to analyze medical images in parallel efficiently and in real-time. The project started by conducting a literature survey in deep learning algorithms and deploying algorithms in tightly constrained environments such as clinical environments. The findings were combined to create an AI-based software architecture that analyzes microcirculation images efficiently and provides results in real time.

The output of the Ph.D. will be a well-researched software architecture for AI-based, efficient, real-time, resource-limited medical image analysis specifically for microcirculation analysis. The impacts will be: - A literature review of methods used to classify and quantify capillaries, - A novel method to detect and quantify the dimensions of capillaries, - A method to detect and quantify the velocity classification of capillaries and - An open source software that can detect and classify image capillaries.

Deep learning is becoming one of the top choices for analyzing medical images. The main attribute of success is the ability of the algorithms to automate the analysis of medical images in a fast and iterable manner. Moreover, such technologies have gained momentum recently due to the development of GPU and the availability of cloud computing resources. Convolutional Neural Networks and Recurrent Neural Networks are popular architectural models in medical image analysis. However, a significant limitation of such technologies is the processing time and unexplainable inaccuracies in real-world datasets. Therefore the most critical R&D challenge is to research and develop an innovative method to analyze ODI's medical video data in a highly accurate, scalable and flexible manner. The medical video data is captured using an innovative medical device, that has reached a prototype stage. The device is a property of ODI Medical AS. The data captured by the ODI device is in the form of sessions. On average, each session contains 6000 frames that need analysis. The objectives of the Ph.D. are: - Surveying the state of the art for object detection and classification in deep learning algorithms - Researching and developing a novel method in object detection and object classification to analyze medical video data using deep learning algorithms - Surveying the state of the art for large scale deployment of deep learning algorithms - Researching and developing a novel large scale deployment for the deep learning developed - Evaluating and testing the system using a real use case involving healthcare video data received from multiple hospitals in different geographical locations.