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

2020/05/22 by Xavier Rafael-Palou, Rafael-Palou, Xavier, Anton Aubanell +11
Computer Science · Medicine · #AI in cancer detection #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Head and Neck Cancer Studies #Image and Video Processing (eess.IV) #Lung Cancer Diagnosis and Treatment #Radiomics and Machine Learning in Medical Imaging #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2005.11341

openalex publication_date 2020/05/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Nodule malignancy assessment is a complex, time-consuming and error-prone\ntask. Current clinical practice requires measuring changes in size and density\nof the nodule at different time-points. State of the art solutions rely on 3D\nconvolutional neural networks built on pulmonary nodules obtained from single\nCT scan per patient. In this work, we propose a two-stream 3D convolutional\nneural network that predicts malignancy by jointly analyzing two pulmonary\nnodule volumes from the same patient taken at different time-points. Best\nresults achieve 77% of F1-score in test with an increment of 9% and 12% of\nF1-score with respect to the same network trained with images from a single\ntime-point.\n

Related

• Detection, growth quantification and malignancy prediction of pulmonary nodules using deep convolutional networks in follow-up CT scans
• TumorNet: Lung Nodule Characterization Using Multi-View Convolutional Neural Network with Gaussian Process
• Re-Identification and Growth Detection of Pulmonary Nodules without\n Image Registration Using 3D Siamese Neural Networks
• Lung Nodule Classification Using Biomarkers, Volumetric Radiomics and 3D\n CNNs
• Multiview convolutional neural networks for lung nodule classification