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

Mingchen Gao, Ulas Bagci, Le Lu, Aaron Wu, Mario Buty, Hoo-Chang Shin, Holger Roth, Georgios Z. Papadakis, Adrien Depeursinge, Ronald M. Summers, Ziyue Xu, Daniel J. Mollura
2016 Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization  
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
more » ... al 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.
doi:10.1080/21681163.2015.1124249 pmid:29623248 pmcid:PMC5881940 fatcat:fmqhxhd2unb73cqwne3wqgedai