Gorriz Blanch, Marc and Carlier, Axel and Faure, Emmanuel
and Giro I Nieto, Xavier
Cost-Effective Active Learning for Melanoma Segmentation.
(2017)
In: 31st Conference on Machine Learning for Health: Workshop at NIPS 2017 (ML4H 2017), 8 December 2017 - 8 December 2017 (Long Beach, California, United States).
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(Document in English)
PDF (Author's version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader 232kB |
Abstract
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 this https URL :https://marc-gorriz.github.io/CEAL-Medical-Image-Segmentation/.
Item Type: | Conference or Workshop Item (Paper) |
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HAL Id: | hal-02871320 |
Audience (conference): | International conference proceedings |
Uncontrolled Keywords: | |
Institution: | French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE) Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE) Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE) Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE) Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE) Other partners > Universitat Politècnica de Catalunya - UPC (SPAIN) |
Laboratory name: | |
Funders: | Image Processing Group at the Universitat Politècnica de Catalunya (Espagne) - Catalan AGAUR office (Espagne) - Spanish Ministerio de Economia y Competitividad (Espagne) - ERDF : European Regional Development Fund (Europe) - NVIDIA Corporation (Europe) |
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Deposited On: | 11 Jun 2020 14:49 |
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