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Cost-Effective Active Learning for Melanoma Segmentation
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We propose a novel Active Learning framework capable to train effectively a convolutional neural network for semantic segmentation of medical imaging, with a limited amount of training labeled data. Our contribution is a practical Cost-Effective Active Learning approach using dropout at test time as Monte Carlo sampling to model the pixel-wise uncertainty and to analyze the image information to improve the training performance. The source code of this project is available at https://marc-gorriz.github.io/CEAL-Medical-Image-Segmentation/ .
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Cited by 1 Pith paper
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Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation
A structured review of deep learning segmentation techniques for scarce and weak annotations, with cost-gain recommendations.
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