# O-MedAL: Online Active Deep Learning for Medical Image Analysis

arXiv: https://arxiv.org/abs/1908.10508 · HTML: https://mlsanity.com/paper/1908.10508/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2019.

## Built on by (most cited first, 6 of 6)

- [Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics](https://mlsanity.com/paper/2111.13069/) (2021)
- [ProtoAL: Interpretable Deep Active Learning with prototypes for medical imaging](https://mlsanity.com/paper/2404.04736/) (2024)
- [Deep Bayesian Active Learning-to-Rank with Relative Annotation for Estimation of Ulcerative Colitis Severity](https://mlsanity.com/paper/2409.04952/) (2024)
- [Robust Framework for COVID-19 Identification from a Multicenter Dataset of Chest CT Scans](https://mlsanity.com/paper/2109.09241/) (2021)
- [On Principal Curve-Based Classifiers and Similarity-Based Selective Sampling in Time-Series](https://mlsanity.com/paper/2204.04620/) (2022)
- [PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification](https://mlsanity.com/paper/2306.16918/) (2023)

## Abstract (arXiv)

Active Learning methods create an optimized labeled training set from unlabeled data. We introduce a novel Online Active Deep Learning method for Medical Image Analysis. We extend our MedAL active learning framework to present new results in this paper. Our novel sampling method queries the unlabeled examples that maximize the average distance to all training set examples. Our online method enhances performance of its underlying baseline deep network. These novelties contribute significant performance improvements, including improving the model's underlying deep network accuracy by 6.30%, using only 25% of the labeled dataset to achieve baseline accuracy, reducing backpropagated images during training by as much as 67%, and demonstrating robustness to class imbalance in binary and multi-class tasks.