Title

Holistic Classification Of Ct Attenuation Patterns For Interstitial Lung Diseases Via Deep Convolutional Neural Networks

Keywords

convolutional neural network; holistic medical image classification; Interstitial lung disease

Abstract

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 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 manual input ROIs, our problem set-up is significantly more difficult than previous work but can better address the clinical workflow. Qualitative and quantitative results using a publicly available ILD database demonstrate state-of-the-art classification accuracy under the patch-based classification and shows the potential of predicting the ILD type using holistic image.

Publication Date

1-2-2018

Publication Title

Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization

Volume

6

Issue

1

Number of Pages

1-6

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1080/21681163.2015.1124249

Socpus ID

85041046467 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85041046467

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