Machine Learning on Biomedical Images: Interactive Learning, Transfer Learning, Class Imbalance, and Beyond
- Resource Type
- Authors
- Marcia Hon; Naimul Mefraz Khan; Nabila Abraham; Ling Guan
- Source
- MIPR
- Subject
- Computer science
business.industry
media_common.quotation_subject
Volume rendering
Machine learning
computer.software_genre
Interactive Learning
Class imbalance
Segmentation
Quality (business)
Limit (mathematics)
Artificial intelligence
Transfer of learning
Function (engineering)
business
computer
media_common
- Language
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.