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

Autor:	Smailagic, Asim, Costa, Pedro, Gaudio, Alex, Khandelwal, Kartik, Mirshekari, Mostafa, Fagert, Jonathon, Walawalkar, Devesh, Xu, Susu, Galdran, Adrian, Zhang, Pei, Campilho, Aurélio, Noh, Hae Young
Rok vydání:	2019
Předmět:	Computer Science - Machine Learning Computer Science - Computer Vision and Pattern Recognition Electrical Engineering and Systems Science - Image and Video Processing Statistics - Machine Learning
Zdroj:	Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 10.4 (2020): e1353
Druh dokumentu:	Working Paper
DOI:	10.1002/widm.1353
Popis:	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. Comment: 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
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1908.10508 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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