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An Uncertainty-aware Hierarchical Probabilistic Network for Early Prediction, Quantification and Segmentation of Pulmonary Tumour Growth
Early detection and quantification of tumour growth would help clinicians to prescribe more accurate treatments and provide better surgical planning.
Detection, growth quantification and malignancy prediction of pulmonary nodules using deep convolutional networks in follow-up CT scans
We address the problem of supporting radiologists in the longitudinal management of lung cancer.
Pulmonary Nodule Malignancy Classification Using its Temporal Evolution with Two-Stream 3D Convolutional Neural Networks
Nodule malignancy assessment is a complex, time-consuming and error-prone task.
Comparative Analysis of Predictive Methods for Early Assessment of Compliance with Continuous Positive Airway Pressure Therapy
Results of the clinical trial confirmed that month 3 was the time-point with the most accurate classifier reaching an f1-score of 87% and 84% in cross-validation and test.
Re-Identification and Growth Detection of Pulmonary Nodules without Image Registration Using 3D Siamese Neural Networks
Lung cancer follow-up is a complex, error prone, and time consuming task for clinical radiologists.
Integration of Convolutional Neural Networks for Pulmonary Nodule Malignancy Assessment in a Lung Cancer Classification Pipeline
The early identification of malignant pulmonary nodules is critical for better lung cancer prognosis and less invasive chemo or radio therapies.
