Multi-Task Learning for Medical Image Analysis and Data Mining

Jiayi Shen (Ph.D. Student)

Multi-task learning is a fundamental learning paradigm for machine learning, which aims to simultaneously solve multiple related tasks to improve the performance of individual tasks by sharing knowledge. The crux of multi-task learning is to explore task relatedness to improve each individual task, which is non-trivial since the underlying relationships among tasks can be complicated and highly nonlinear. Early works deal with this crux by learning shared features, designing regularizers im- posed on parameters or exploring priors over parameters. Recently, multi-task deep neural networks have been developed, learning shared representations in the feature layers while keeping classifier layers independent. Recently, multi-task learning has generated increasing interest in medical image analysis, e.g., multiple organ localization and direct volume estimation. In the real world, the great power of multi-task learning in medical image analysis has largely been under-developed, due to the data insufficient problem. Therefore, when very limited training data is given, it is highly desired to propose new general frameworks for multi-task learning. In this case, it is difficult to learn a proper model for each task independently without overfitting.

Primary Advisor:	Marcel Worring (University of Amsterdam)
Industry Advisor:	Xiantong Zhen (University of Amsterdam & Inception Institute of Artificial Intelligence)
PhD Duration:	01 June 2020 - 01 June 2024