# Exploring the similarity of medical imaging classification problems

arXiv: https://arxiv.org/abs/1706.03509 · HTML: https://mlsanity.com/paper/1706.03509/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2017.

## Built on by (most cited first, 4 of 4)

- [Task Fingerprinting for Meta Learning in Biomedical Image Analysis](https://mlsanity.com/paper/2107.03949/) (2021)
- [Beyond Knowledge Silos: Task Fingerprinting for Democratization of Medical Imaging AI](https://mlsanity.com/paper/2412.08763/) (2024)
- [Meta-learning for skin cancer detection using Deep Learning Techniques](https://mlsanity.com/paper/2104.10775/) (2021)
- [Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classification](https://mlsanity.com/paper/2107.05940/) (2021)

## Abstract (arXiv)

Supervised learning is ubiquitous in medical image analysis. In this paper we consider the problem of meta-learning -- predicting which methods will perform well in an unseen classification problem, given previous experience with other classification problems. We investigate the first step of such an approach: how to quantify the similarity of different classification problems. We characterize datasets sampled from six classification problems by performance ranks of simple classifiers, and define the similarity by the inverse of Euclidean distance in this meta-feature space. We visualize the similarities in a 2D space, where meaningful clusters start to emerge, and show that the proposed representation can be used to classify datasets according to their origin with 89.3% accuracy. These findings, together with the observations of recent trends in machine learning, suggest that meta-learning could be a valuable tool for the medical imaging community.