Pulmonary nodule type classification with convolutional networks

F. Ciompi, K. Chung, A.A.A. Setio, S.J. van Riel, E.T. Scholten, P.K. Gerke, C. Jacobs, U. Pastorino, A. Marchiano, M.M.W. Wille, M. Prokop and B. van Ginneken

in: Medical Image Computing and Computer-Assisted Intervention, 2016

Abstract

Classification of detected pulmonary nodules is a key task in deciding the optimal follow-up strategy for patients in lung cancer screening. We propose a framework based on Convolutional Networks (ConvNets) to automatically assess nodule type for lesions detected in CT scans. The proposed ConvNet processes nodules in 3D scans through a combination of several 2D views and classifies it as solid, part-solid, non-solid and calcified. We validated the method on data from the lung cancer screening trials DLCST and MILD.

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