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Cost-Effective Active Learning for Melanoma Segmentation

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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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)
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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