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

arXiv: https://arxiv.org/abs/1902.05908 · HTML: https://mlsanity.com/paper/1902.05908/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2019.

## Built on by (most cited first, 1 of 1)

- [A Comparative Analysis of Machine Learning and Grey Models](https://mlsanity.com/paper/2104.00871/) (2021)

## Abstract (arXiv)

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.