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O-MedAL: Online active deep learning for medical image analysis - 2020

Research Area:  Machine Learning

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 models 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.

Author(s) Name:   Asim Smailagic, Pedro Costa, Alex Gaudio, Kartik Khandelwal, Mostafa Mirshekari, Jonathon Fagert, Devesh Walawalkar, Susu Xu, Adrian Galdran, Pei Zhang, AurĂ©lio Campilho, Hae Young Noh

Journal name:  WIREs Data Mining and Knowledge Discovery

Conferrence name:  

Publisher name:  Wiley

DOI:  10.1002/widm.1353

Volume Information:  Volume10, Issue4 July/August 2020