Learning Algorithms for Predictive Modeling in Biomedical Computing: Methods and Applications

点击翻译按钮获取中文摘要

Computer Aided Interventions (CAI) refer to systems that incorporate information from a multitude of biosensors, with computational algorithms to assist in decision making. CAI systems are a cornerstone of modern medicine and have played a significant role for the past few decades in areas such as radiology and robotic surgery. The vast amount of data generated from various biosensors and tools (e.g. imaging modalities) for CAI are complemented by unprecedented advances in computational approaches in particular machine learning for analysis of data. Understanding biomedical data has therefore been impacted fundamentally, mostly in a positive manner, with this trend expected to grow. However, in practice, the abundance of machine learning solutions stands in stark contrast to their uptake in decision making with practical impact. Of the significant challenges for realizing this are the noisy and highly heterogenous nature of the data from various modalities and populations, and the limited availability and inaccurate nature of annotations. The overarching goal of this proposal is to design the next generation of learning algorithms and critical decision-making approaches for actionable, optimized CAI that address the challenges to the uptake of the developed techniques in this domain. I propose innovative methods that: i) disentangle informative task-specific attributes of data from modality-specific attributes. Task-specific attributes can then be used to ensure knowledge learned from one domain of data (e.g. imaging modality such as raw US) is transferable to other domains (e.g. B-mode US). To fuse multiple modalities of data, flexible and efficient deep learning-based approaches that eliminate optimization during registration and provide uncertainty estimates are devised; ii) involve discriminative and generative approaches to learn from data with imprecise annotations through unsupervised discovery of associations between data points, and decision making using such associations in the context of limited available gold-standard annotations; iii) provide decision support for tissue classification using simultaneous theory-guided and data-driven learning. Theory-based, simulated data allow convergence to a solution and experimental data help minimize the residual error from the previous step, through unsupervised adversarial methods. The proposed research program will provide training for 3 PhD, 3 MSc and 5UG HQP. State of the art interdisciplinary training environment for HQP that follows the principals of Equity, Diversity and Inclusion will be provided. Trainees will acquire a wide spectrum of skills including image processing, machine learning, decision making and software development. They will learn to translate their knowledge of computing to high impact problems with practical implications. Their training will be of considerable value to a growing demand in both public and private sectors in machine learning and biomedicine.