Using ensemble radiomic models to identify uncertainty in lung nodule classifications
Authors:
- Elisabeth PfaehlerEPElisabeth Pfaehler
- Lena KriegerLKLena Krieger
- Ira AssentIAIra Assent
- Sven NebelungSNSven Nebelung
- Alex ZwanenburgAZAlex Zwanenburg
- Daniel TruhnDTDaniel Truhn
Open Access
- 22 October 2025
- journal article
- Published by Elsevier in European Journal of Radiology Artificial Intelligence
- Vol. 4
Abstract
No abstract availableThis publication has 24 references indexed in Scilit:
- Lung nodule classification using radiomics model trained on degraded SDCT imagesComputer Methods and Programs in Biomedicine, 2024
- Uncertainty-aware image classification on 3D CT lungComputers in Biology and Medicine, 2024
- A survey of uncertainty in deep neural networksArtificial Intelligence Review, 2023
- A radiomics-based decision support tool improves lung cancer diagnosis in combination with the Herder score in large lung nodulesEBioMedicine, 2022
- Lung Nodule Classification Using Biomarkers, Volumetric Radiomics, and 3D CNNsJournal of Digital Imaging, 2021
- Plausibility and redundancy analysis to select FDG‐PET textural features in non‐small cell lung cancerMedical Physics, 2020
- External validation of radiomics‐based predictive models in low‐dose CT screening for early lung cancer diagnosisMedical Physics, 2020
- Uncertainty Estimates and Multi-hypotheses Networks for Optical Flow2018
- Prediction of risk of lung cancer in populations and in pulmonary nodules: Significant progress to drive changes in paradigmsLung Cancer, 2015
- Radiomics: Extracting more information from medical images using advanced feature analysisEuropean Journal of Cancer, 2012