O-MedAL: Online Active Deep Learning for Medical Image Analysis
Abstract
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.
Cite
@article{arxiv.1908.10508,
title = {O-MedAL: Online Active Deep Learning for Medical Image Analysis},
author = {Asim Smailagic and Pedro Costa and Alex Gaudio and Kartik Khandelwal and Mostafa Mirshekari and Jonathon Fagert and Devesh Walawalkar and Susu Xu and Adrian Galdran and Pei Zhang and Aurélio Campilho and Hae Young Noh},
journal= {arXiv preprint arXiv:1908.10508},
year = {2020}
}
Comments
Code: https://github.com/adgaudio/o-medal ; Accepted and published by Wiley Journal of Pattern Recognition and Knowledge Discovery ; Journal URL: https://doi.org/10.1002/widm.1353