Development of lung nodule detection algorithms in chest CT
Automatic detection of lung nodules using pattern recognition and deep learning algorithms
Posts by Collection
portfolio
Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in CT images
Large-scale evaluation of algorithms for automatic detection of lung nodules. 
Pulmonary nodules malignancy prediction in CT images
Automatic lung nodule malignancy prediction using deep learning. 
publications
Optimized 8-level turbo encoder algorithm and VLSI architecture for LTE
Published in International Conference on Electrical Engineering and Informatics (ICEEI), 2011
An optimized algorithm and VLSI architecture for LTE turbo encoding.
Evaluation and comparison of textural feature representation for the detection of early stage cancer in endoscopy
Published in Proceedings of VISAPP, 2013
Evaluates textural feature representations for early-stage cancer detection in endoscopy images.
Memory-centric accelerator design for Convolutional Neural Networks
Published in IEEE 31st International Conference on Computer Design (ICCD), 2013
A memory-centric hardware accelerator design for efficient CNN inference.
Computer-aided detection of lung cancer: combining pulmonary nodule detection systems with a tumor risk prediction model
Published in SPIE Medical Imaging, 2015
Combines multiple nodule detection systems with a tumor risk model for improved lung cancer CAD.
Off-the-shelf Convolutional Neural Network features for pulmonary nodule detection in computed tomography scans
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.
Automatic detection of large pulmonary solid nodules in thoracic CT images
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.
Deep convolutional neural networks for automatic coronary calcium scoring in a screening study with low-dose chest CT
Published in SPIE Medical Imaging, 2016
Applies deep CNNs to automatically score coronary artery calcification in low-dose chest CT screening.
Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks
Published in IEEE Transactions on Medical Imaging, 2016
A multi-view CNN approach that significantly reduces false positives in automated pulmonary nodule detection.
Towards automatic pulmonary nodule management in lung cancer screening with deep learning
Published in Scientific Reports, 2017
A deep learning system for automated pulmonary nodule management following lung cancer screening guidelines.
Using deep learning to segment breast and fibroglandular tissue in MRI volumes
Published in Medical Physics, 2017
Applies deep learning to automatically segment breast and fibroglandular tissue in MRI for density assessment.
Improving airway segmentation in computed tomography using leak detection with convolutional networks
Published in Medical Image Analysis, 2017
Uses convolutional networks to detect and prevent leakage in automated airway segmentation from chest CT.
Organ detection in thorax abdomen CT using multi-label convolutional neural networks
Published in SPIE Medical Imaging, 2017
Multi-label CNNs for simultaneous detection and localization of multiple organs in thorax-abdomen CT.
Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge
Published in Medical Image Analysis, 2017
The LUNA16 grand challenge: benchmarking state-of-the-art algorithms for pulmonary nodule detection across 888 CT scans.
A survey on deep learning in medical image analysis
Published in Medical Image Analysis, 2017
A comprehensive survey covering deep learning applications across medical image analysis tasks including classification, detection, and segmentation.
Computer-aided diagnosis in thoracic CT scans for lung cancer screening
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.
Efficient organ localization using multi-label convolutional neural networks in thorax-abdomen CT scans
Published in Physics in Medicine & Biology, 2018
Efficient multi-label CNN approach for fast and accurate localization of multiple organs in thorax-abdomen CT.
Class-aware adversarial lung nodule synthesis in CT images
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.
Extracting and leveraging nodule features with lung inpainting for local feature augmentation
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.
Synthetic database of aortic morphometry and hemodynamics: overcoming medical imaging data availability
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.
Robust classification from noisy labels: Integrating additional knowledge for chest radiography abnormality assessment
Published in Medical Image Analysis, 2021
Integrates prior medical knowledge with noisy label learning to robustly classify abnormalities in chest X-rays.
Deep learning for malignancy risk estimation of pulmonary nodules detected at low-dose screening CT
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.
Deep Learning Based Centerline-Aggregated Aortic Hemodynamics: An Efficient Alternative to Numerical Modelling of Hemodynamics
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.
Deep learning for lung cancer detection on screening CT scans: results of a large-scale public competition and an observer study with 11 radiologists
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.
Accuracy of artificial intelligence model for infectious keratitis classification: a systematic review and meta-analysis
Published in Frontiers in Public Health, 2023
Systematic review and meta-analysis evaluating the diagnostic accuracy of AI models for classifying infectious keratitis.
SPEC-CXR: Advancing Clinical Safety Through Entity-Level Performance Evaluation of Chest X-ray Report Generation
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.
Beyond One Size Fits All: Customization of Radiology Report Generation Methods
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.
Deep learning-based pulmonary nodule risk assessment outperforms established malignancy risk scores in lung cancer screening
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.
talks
Talk 1 on Relevant Topic in Your Field
Published:
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Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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