Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks

Gao, Mingchen (National Institutes of Health (NIH), Bethesda, MD 20892, US) ; Bagci, Ulas (2 University of Central Florida (UCF), Orlando, FL 32816, US) ; Depeursinge, Adrien (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; et al.

Interstitial lung diseases (ILD) involve several abnormal imaging patterns observed in computed tomography (CT) images. Accurate classification of these patterns plays a significant role in precise clinical decision making of the extent and nature of the diseases. Therefore it is important for developing automated pulmonary computer-aided detection (CAD) systems. Conventionally, this task relies on experts’ manual identification of regions of interest (ROIs) as a prerequisite to diagnose potential diseases. This protocol is time consuming and inhibits fully automatic assessment. In this paper, we present a new method to classify ILD imaging patterns on CT images. The main difference is that the proposed algorithm uses the entire image as a holistic input. By circumventing the prerequisite of manually input ROIs, our problem setup is significantly more difficult than previous work but can better address the clinical workflow. Qualitative and quantitative results using a publicly available ILD database demonstrates state-of-the-art classification accuracy under the patch based classification and shows the potential of predicting the ILD type using holistic image.


Keywords:
Conference Type:
full paper
Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Economie/gestion
Publisher:
Munich, Germany
Date:
Munich
Germany
2015
Pagination:
8 p.
Published in:
Proceedings of MICCAI 2015
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2016-04-28, last modified 2019-06-11

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