@inproceedings{6a2526f5a8ef4f1983386db9b1204f55,
title = "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.",
keywords = "Convolutional networks, Deep learning, Emphysema, Fibrosis, Ground glass, Infiltrative lung diseases, Lung texture classification",
author = "Tarando, \{Sebastian Roberto\} and Catalin Fetita and Brillet, \{Pierre Yves\}",
note = "Publisher Copyright: {\textcopyright} 2017 SPIE.; Medical Imaging 2017: Computer-Aided Diagnosis ; Conference date: 13-02-2017 Through 16-02-2017",
year = "2017",
month = jan,
day = "1",
doi = "10.1117/12.2255552",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Petrick, \{Nicholas A.\} and Armato, \{Samuel G.\}",
booktitle = "Medical Imaging 2017",
}