Pulmonary Nodule Malignancy Classification Using its Temporal Evolution with Two-Stream 3D Convolutional Neural Networks

Xavier Rafael-Palou, Anton Aubanell, Ilaria Bonavita, Mario Ceresa, Gemma Piella, Vicent Ribas, Miguel Ángel González Ballester

Abstract: Nodule malignancy assessment is a complex, time-consuming and error-prone task. Current clinical practice requires measuring changes in size and density of the nodule at different time-points. State of the art solutions rely on 3D convolutional neural networks built on pulmonary nodules obtained from a single CT scan per patient. In this work, we propose a two-stream 3D convolutional neural network that predicts malignancy by jointly analyzing two pulmonary nodule volumes from the same patient taken at different time-points. Best results achieve 77% of F1-score in test with an increment of 9% and 12% of F1-score with respect to the same network trained with images from a single time-point.

Paper Type: both

TL;DR: A two-stream 3D CNN that predicts malignancy by jointly analyzing two pulmonary nodule volumes from the same patient taken at different time-points

Track: short paper

Keywords: Lung Cancer, Pulmonary Nodule Malignancy, Convolutional Neural Networks

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