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.
@article{arxiv.1902.05908,
title = {Machine Learning on Biomedical Images: Interactive Learning, Transfer Learning, Class Imbalance, and Beyond},
author = {Naimul Mefraz Khan and Nabila Abraham and Ling Guan},
journal= {arXiv preprint arXiv:1902.05908},
year = {2019}
}
Comments
Accepted at IEEE MIPR 2019. arXiv admin note: text overlap with arXiv:1810.07842