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

生物医学计算中预测建模的学习算法：方法与应用

• 批准号: RGPIN-2020-07117
• 负责人: Mousavi, Parvin
• 金额: $ 3.5万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=718698
• 关键词: Learning; Algorithms; Predictive; Modeling; Biomedical

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.

计算机辅助干预（CAI）是指将来自众多生物传感器的信息与辅助决策的计算算法相结合的系统。CAI系统是现代医学的基石，在过去几十年中在放射学和机器人手术等领域发挥了重要作用。从各种生物传感器和工具（例如成像模式）为CAI生成的大量数据与计算方法的前所未有的进步相辅相成，特别是用于数据分析的机器学习。因此，对生物医学数据的理解受到了根本性的影响，主要是积极的影响，预计这一趋势将继续增长。然而，在实践中，机器学习解决方案的丰富与它们在决策中的实际影响形成鲜明对比。实现这一点的重大挑战之一是来自各种模式和群体的数据的嘈杂和高度异构性，以及注释的有限可用性和不准确性。