Simple uncertainty-based active learning with a weighted loss matched full-data COVID-19 CXR severity classifiers using 15.4% (binary) and 23.1% (multi-class) of training labels.
Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network
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abstract
Training robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity. We propose an active learning (AL) framework to select most informative samples and add to the training data. We use conditional generative adversarial networks (cGANs) to generate realistic chest xray images with different disease characteristics by conditioning its generation on a real image sample. Informative samples to add to the training set are identified using a Bayesian neural network. Experiments show our proposed AL framework is able to achieve state of the art performance by using about 35% of the full dataset, thus saving significant time and effort over conventional methods.
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Deep Active Learning for Lung Disease Severity Classification from Chest X-rays: Learning with Less Data in the Presence of Class Imbalance
Simple uncertainty-based active learning with a weighted loss matched full-data COVID-19 CXR severity classifiers using 15.4% (binary) and 23.1% (multi-class) of training labels.