Data-Efficient Medical Image Classification using Learned Uncertainty

使用习得不确定性进行数据高效的医学图像分类

• 批准号: 486617
• 负责人: Du Tian Xiang
• 金额: $ 1.27万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Studentship Programs
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-12-01 至 2023-12-01
• 项目状态: 已结题
• 来源: https://webapps.cihr-irsc.gc.ca/decisions/p/project_details.html?applId=486617&lang=en
• 关键词: Convolutional Neural Networks; Deep Learning; Diagnostic Classification; Image Classification; Irritable Bowel Disease; Machine Learning; Model Distillation; Probabilistic Labels; Sample Efficient; Transfer Learning

In recent years, medicine has witnessed the promising potential of machine learning (ML). ML, particularly deep ML, is well-suited to medical diagnostics given the high dimensionality and growing availability of patient data. Despite increased data curati

近年来，医学见证了机器学习（ML）的巨大潜力。考虑到患者数据的高维性和日益增长的可用性，ML，特别是深度ML，非常适合医疗诊断。尽管增加了数据管理