Robust classification techniques for medical imaging

医学成像的稳健分类技术

• 批准号: RGPIN-2016-06283
• 负责人: Martel, Anne
• 金额: $ 2.4万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=633045
• 关键词: Robust; classification; techniques; medical; imaging

Machine learning techniques have had an enormous impact on the field of medical image analysis and have been successfully applied to a wide variety of classification, segmentation and registration problems. There are, however, significant challenges in taking a classification algorithm trained on one specific research dataset and translating it to routine clinical practice where images acquired at different centres in different patient populations may have very different characteristics. The problem is made even more difficult by the high cost of collecting good quality training images complete with expert annotations or ground truth diagnoses.

机器学习技术对医学图像分析领域产生了巨大的影响，并已成功应用于各种分类，分割和配准问题。然而，在采用在一个特定研究数据集上训练的分类算法并将其转化为常规临床实践方面存在重大挑战，其中在不同患者人群的不同中心获得的图像可能具有非常不同的特征。由于收集具有专家注释或地面实况诊断的高质量训练图像的高成本，该问题变得更加困难。