O-MedAL: Online active deep learning for medical image analysis

Authors: Smailagic, A., Galdran, A. et al.

Journal: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Volume: 10

Issue: 4

eISSN: 1942-4795

ISSN: 1942-4787

DOI: 10.1002/widm.1353

Abstract:

Active learning (AL) 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 AL framework to present new results in this paper. A 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 to 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 multiclass tasks. This article is categorized under:. Technologies > Machine Learning. Technologies > Classification. Application Areas > Health Care.

Source: Scopus

O-MedAL: Online active deep learning for medical image analysis

Authors: Smailagic, A., Galdran, A. et al.

Journal: WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY

Volume: 10

Issue: 4

eISSN: 1942-4795

ISSN: 1942-4787

DOI: 10.1002/widm.1353

Source: Web of Science (Lite)

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

Authors: Smailagic, A., Galdran, A. et al.

Journal: CoRR

Volume: abs/1908.10508

Source: DBLP

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

Authors: Smailagic, A., Galdran, A. et al.

Journal: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 10.4 (2020): e1353

DOI: 10.1002/widm.1353

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.

http://dx.doi.org/10.1002/widm.1353

Source: arXiv