Cascade of convolutional neural networks for lung texture classification: overcoming ontological overlapping

Abstract : The infiltrative lung diseases are a class of irreversible, non-neoplastic lung pathologies requiring regular follow-up with CT imaging. Quantifying the evolution of the patient status imposes the development of automated classification tools for lung texture. Traditionally, such classification relies on a two-dimensional analysis of axial CT images. This paper proposes a cascade of the existing CNN based CAD system, specifically tuned-up. The advantage of using a deep learning approach is a better regularization of the classification output. In a preliminary evaluation, the combined approach was tested on a 13 patient database of various lung pathologies, showing an increase of 10% in True Positive Rate (TPR) with respect to the best suited state of the art CNN for this task
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Communication dans un congrès
SPIE Medical Imaging 2017: Computer-Aided Diagnosis, Feb 2017, Orlando, United States. SPIE, Proceedings SPIE Medical Imaging 2017: Computer-Aided Diagnosis, 10134, pp.1013407-1 - 1013407-9, 2017, 〈10.1117/12.2255552〉
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https://hal.archives-ouvertes.fr/hal-01686291
Contributeur : Médiathèque Télécom Sudparis & Institut Mines-Télécom Business School <>
Soumis le : mercredi 17 janvier 2018 - 11:17:30
Dernière modification le : jeudi 31 mai 2018 - 09:12:02

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Sebastian Roberto Tarando, Catalin Fetita, Pierre Yves Brillet. Cascade of convolutional neural networks for lung texture classification: overcoming ontological overlapping. SPIE Medical Imaging 2017: Computer-Aided Diagnosis, Feb 2017, Orlando, United States. SPIE, Proceedings SPIE Medical Imaging 2017: Computer-Aided Diagnosis, 10134, pp.1013407-1 - 1013407-9, 2017, 〈10.1117/12.2255552〉. 〈hal-01686291〉

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