Machine Learning on Biomedical Images: Interactive Learning, Transfer Learning, Class Imbalance, and Beyond
This work tackles practical bottlenecks in biomedical image analysis, though it appears incremental as it builds on existing methods.
The paper addresses three limitations in biomedical image analysis through case studies: interactive machine learning reduces exploration time for volume rendering, transfer learning with preprocessing improves Alzheimer's diagnosis with smaller datasets, and a novel focal Tversky loss function enhances segmentation results for imbalanced data.
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.