Machine Learning on Biomedical Images: Interactive Learning, Transfer Learning, Class Imbalance, and Beyond

被引：9

作者：

Khan, Naimul Mefraz ^{[1
]}

Abraham, Nabila ^{[1
]}

Hon, Marcia ^{[1
]}

Guan, Ling ^{[1
]}

机构：

[1] Ryerson Univ, Ryerson Multimedia Res Lab, Toronto, ON, Canada

来源：

2019 2ND IEEE CONFERENCE ON MULTIMEDIA INFORMATION PROCESSING AND RETRIEVAL (MIPR 2019) | 2019年

关键词：

MRI;

D O I：

10.1109/MIPR.2019.00023

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this paper, we highlight three issues that limit performance of machine learning on biomedical images, and tackle them through 3 case studies: 1) Interactive Machine Learning (IML): we show how IML can drastically improve exploration time and quality of direct volume rendering. 2) transfer learning: we show how transfer learning along with intelligent pre-processing can result in better Alzheimer's diagnosis using a much smaller training set 3) data imbalance: we show how our novel focal Tversky loss function can provide better segmentation results taking into account the imbalanced nature of segmentation datasets. The case studies are accompanied by in-depth analytical discussion of results with possible future directions.

引用

页码：85 / 90

页数：6