MIDL 2020, S094, Rafael-Palou et al. Short presentation
Channel: MIDL Society
Date: 05/07/2020

S094 - 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

Conference page: https://2020.midl.io/papers/rafael-palou20.html
PDF: https://openreview.net/pdf?id=D1jTt_FOPY

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.