Increasing CAD system efficacy for lung texture analysis using a convolutional network

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. For the large majority of CAD systems, such classification relies on a two-dimensional analysis of axial CT images. In a previously developed CAD system, we proposed a fully-3D approach exploiting a multi-scale morphological analysis which showed good performance in detecting diseased areas, but with a major drawback consisting of sometimes overestimating the pathological areas and mixing different type of lung patterns. This paper proposes a combination of the existing CAD system with the classification outcome provided by a convolutional network, specifically tuned-up, in order to increase the specificity of the classification and the confidence to diagnosis. The advantage of using a deep learning approach is a better regularization of the classification output (because of a deeper insight into a given pathological class over a large series of samples) where the previous system is extra-sensitive due to the multi-scale response on patient-specific, localized patterns. In a preliminary evaluation, the combined approach was tested on a 10 patient database of various lung pathologies, showing a sharp increase of true detections.
Type de document :
Communication dans un congrès
SPIE Medical Imaging, Feb 2016, Orlando, United States. 9785, pp.97850Q, 2016, Medical Imaging 2016: Computer-Aided Diagnosis. 〈10.1117/12.2217752〉
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https://hal.archives-ouvertes.fr/hal-01443107
Contributeur : Catalin Fetita <>
Soumis le : dimanche 22 janvier 2017 - 14:33:08
Dernière modification le : lundi 4 décembre 2017 - 10:36:47

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Sebastian Tarando, Catalin Fetita, Faccinetto Alex, Pierre-Yves Brillet. Increasing CAD system efficacy for lung texture analysis using a convolutional network. SPIE Medical Imaging, Feb 2016, Orlando, United States. 9785, pp.97850Q, 2016, Medical Imaging 2016: Computer-Aided Diagnosis. 〈10.1117/12.2217752〉. 〈hal-01443107〉

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