Also available on Google Scholar and ORCID.
Published in Radiology Advances, 2026
A deep learning risk assessment model for pulmonary nodules that outperforms established clinical risk scores (Lung-RADS, Mayo Clinic) in a multi-center lung cancer screening cohort.
Published in Frontiers in Public Health, 2023
Systematic review and meta-analysis evaluating the diagnostic accuracy of AI models for classifying infectious keratitis.
Published in Radiology: Artificial Intelligence, 2021
A large-scale evaluation comparing deep learning systems against 11 radiologists for lung cancer detection on screening CT, showing competitive AI performance.
Published in IEEE Journal of Biomedical and Health Informatics, 2021
A deep learning approach that aggregates aortic centerline features to efficiently predict hemodynamic parameters, replacing costly CFD simulations.
Published in Radiology, 2021
A deep learning model that outperforms radiologists and existing risk models in estimating malignancy risk of pulmonary nodules at CT screening.
Published in Medical Image Analysis, 2021
Integrates prior medical knowledge with noisy label learning to robustly classify abnormalities in chest X-rays.
Published in IEEE Transactions on Medical Imaging, 2021
Creates a large synthetic dataset of aortic morphology and hemodynamics to address data scarcity in cardiovascular AI research.
Published in Physics in Medicine & Biology, 2018
Efficient multi-label CNN approach for fast and accurate localization of multiple organs in thorax-abdomen CT.
Published in Medical Image Analysis, 2017
A comprehensive survey covering deep learning applications across medical image analysis tasks including classification, detection, and segmentation.
Published in Medical Image Analysis, 2017
The LUNA16 grand challenge: benchmarking state-of-the-art algorithms for pulmonary nodule detection across 888 CT scans.
Published in Medical Image Analysis, 2017
Uses convolutional networks to detect and prevent leakage in automated airway segmentation from chest CT.
Published in Medical Physics, 2017
Applies deep learning to automatically segment breast and fibroglandular tissue in MRI for density assessment.
Published in Scientific Reports, 2017
A deep learning system for automated pulmonary nodule management following lung cancer screening guidelines.
Published in IEEE Transactions on Medical Imaging, 2016
A multi-view CNN approach that significantly reduces false positives in automated pulmonary nodule detection.
Published in Medical Physics, 2015
An automated method for detecting large solid pulmonary nodules in thoracic CT using multi-scale Hessian filtering and a classification pipeline.
Published in International Workshop on Agentic AI for Medicine (MICCAI), 2025
Explores customization strategies for radiology report generation models to accommodate different clinical workflows and institutional requirements.
Published in International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2025
Proposes an entity-level evaluation framework for chest X-ray report generation to better assess clinical safety of AI-generated radiology reports.
Published in International Workshop on Machine Learning in Medical Imaging (MLMI), 2020
Uses lung inpainting to extract and transfer nodule features for data augmentation in nodule detection models.
Published in IEEE 16th International Symposium on Biomedical Imaging (ISBI), 2019
A class-aware GAN for synthesizing realistic lung nodules in CT to augment training data for detection and classification.
Published in SPIE Medical Imaging, 2017
Multi-label CNNs for simultaneous detection and localization of multiple organs in thorax-abdomen CT.
Published in SPIE Medical Imaging, 2016
Applies deep CNNs to automatically score coronary artery calcification in low-dose chest CT screening.
Published in IEEE International Symposium on Biomedical Imaging (ISBI), 2015
Demonstrates that pre-trained CNN features can be effectively repurposed for pulmonary nodule detection in CT.
Published in SPIE Medical Imaging, 2015
Combines multiple nodule detection systems with a tumor risk model for improved lung cancer CAD.
Published in IEEE 31st International Conference on Computer Design (ICCD), 2013
A memory-centric hardware accelerator design for efficient CNN inference.
Published in Proceedings of VISAPP, 2013
Evaluates textural feature representations for early-stage cancer detection in endoscopy images.
Published in International Conference on Electrical Engineering and Informatics (ICEEI), 2011
An optimized algorithm and VLSI architecture for LTE turbo encoding.
Published in PhD Thesis, Radboud University Nijmegen, 2018
PhD thesis on deep learning methods for automated detection and characterization of pulmonary nodules in CT for lung cancer screening.