Colin Jacobs

Assistant Professor

Radboudumc

Netherlands

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Colin Jacobs is an Assistant Professor at Radboudumc in the Netherlands. His research focuses on lung cancer screening, low-dose CT imaging, and the application of artificial intelligence in radiology. He has contributed to various articles discussing training data's impact on generalizability, performance evaluation of deep learning models for pulmonary nodule malignancy estimation, and management recommendations for lung cancer screening.

Colin Jacobs

Research Interests

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Articles (22)

Characterizing the Impact of Training Data on Generalizability: Application in Deep Learning to Estimate Lung Nodule Malignancy Risk

Towards safe and reliable deep learning for lung nodule malignancy estimation using out-of-distribution detection

ESR Essentials: lung cancer screening with low-dose CT—practice recommendations by the European Society of Thoracic Imaging

Artificial intelligence in radiology: 173 commercially available products and their scientific evidence

Performance of a screening-trained DL model for pulmonary nodule malignancy estimation of incidental clinical nodules

Aggressiveness-guided nodule management for lung cancer screening in Europe—justification for follow-up intervals and definition of growth

Lung cancer screening with low-dose CT: definition of positive, indeterminate, and negative screen results. A nodule management recommendation from the European Society of Thoracic Imaging

Artificial intelligence for the detection of airway nodules in chest CT scans

Structure and position-aware graph neural network for airway labeling

Decoding pulmonary nodules: can machine learning enhance malignancy risk stratification?

An AI deep learning algorithm for detecting pulmonary nodules on ultra-low-dose CT in an emergency setting: a reader study

Lung Nodule Management in Low-Dose CT Screening for Lung Cancer: Lessons from the NELSON Trial

Prior CT Improves Deep Learning for Malignancy Risk Estimation of Screening-detected Pulmonary Nodules

Prediction Variability to Identify Reduced AI Performance in Cancer Diagnosis at MRI and CT

Challenges and outlook in the management of pulmonary nodules detected on CT

Dense regression activation maps for lesion segmentation in CT scans of COVID-19 patients

Innovations in thoracic imaging: CT, radiomics, AI and x‐ray velocimetry

Scan-based competing death risk model for re-evaluating lung cancer computed tomography screening eligibility

Automated COVID-19 Grading With Convolutional Neural Networks in Computed Tomography Scans: A Systematic Comparison

Deep Learning for Lung Cancer Detection on Screening CT Scans: Results of a Large-Scale Public Competition and an Observer Study with 11 Radiologists

Combining pulmonary and cardiac computed tomography biomarkers for disease-specific risk modelling in lung cancer screening

Assisted versus Manual Interpretation of Low-Dose CT Scans for Lung Cancer Screening: Impact on Lung-RADS Agreement

Collaborators (7)

Joachim G. Aerts

John J. Hermans

Mario Silva

Clara Isabel Sánchez Gutiérrez

Henkjan Huisman

Zaigham Saghir

Rozemarijn Vliegenthart

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