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2017

Cost-Effective Active Learning for Melanoma Segmentation

Gorriz, Marc, Carlier, Axel, Faure, Emmanuel et al.

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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/ .

Cost-Effective Active Learning for Melanoma Segmentation · Around

Built on

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