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

• Published in: Computer methods in biomechanics and biomedical engineering. 2018-01, Vol.6 (1), p.1-6
• Main Authors: Gao, Mingchen, Bagci, Ulas, Lu, Le, Wu, Aaron, Buty, Mario, Shin, Hoo-Chang, Roth, Holger, Papadakis, Georgios Z., Depeursinge, Adrien, Summers, Ronald M., Xu, Ziyue, Mollura, Daniel J.
• Format: Article
• Language: English
• Subjects: convolutional neural network; holistic medical image classification; Interstitial lung disease
• ISSN: 2168-1163; 2168-1171
• DOI: 10.1080/21681163.2015.1124249

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.