Publications of Dré Peeters

all all years 2026 2025 2024 2023

Papers in international journals

  1. R. Dinnessen, D. Peeters, N. Antonissen, F. Mohamed Hoesein, H. Gietema, E. Scholten, C. Schaefer-Prokop and C. Jacobs, "Performance of a screening-trained DL model for pulmonary nodule malignancy estimation of incidental clinical nodules", European Radiology, 2025.
    Abstract DOI PMID
  2. D. Peeters, K. Venkadesh, R. Dinnessen, Z. Saghir, E. Scholten, R. Vliegenthart, M. Prokop and C. Jacobs, "Towards safe and reliable deep learning for lung nodule malignancy estimation using out-of-distribution detection", Computers in Biology and Medicine, 2025;186:109633.
    Abstract DOI PMID
  3. D. Peeters, N. Alves, K. Venkadesh, R. Dinnessen, Z. Saghir, E. Scholten, C. Schaefer-Prokop, R. Vliegenthart, M. Prokop and C. Jacobs, "Enhancing a deep learning model for pulmonary nodule malignancy risk estimation in chest CT with uncertainty estimation", European Radiology, 2024;34:6639-6651.
    Abstract DOI PMID

Papers in conference proceedings

  1. J. Tagscherer, S. de Boer, L. Philipp, F. van der Graaf, D. Peeters, J. Bosma, L. Leijten, B. Obreja, E. Smit and A. Hering, "EvalBlocks: A Modular Pipeline for Rapidly Evaluating Foundation Models in Medical Imaging", 2026.
    arXiv
  2. J.S. Bosma, D. Peeters, N. Alves, A. Saha, Z. Saghir, C. Jacobs and H. Huisman, "Reproducibility of Training Deep Learning Models for Medical Image Analysis", Medical Imaging with Deep Learning, 2023.
    Abstract Url

Abstracts

  1. R. Dinnessen, A. Antonissen, D. Peeters, H. Gietema, E. Scholten, C. Schaefer-Prokop and C. Jacobs, "Exploring AI-enabled nodule management for incidentally detected pulmonary nodules on CT.", Annual Meeting of the European Society of Thoracic Imaging, 2025.
    Abstract
  2. N. Antonissen, R. Dinnessen, D. Peeters, E. Scholten, F. Mohamed Hoesein, R. Vliegenthart, H. Gietema, C. Schaefer-Prokop, M. Prokop and C. Jacobs, "External test of a deep learning model incorporating prior imaging for risk stratification of persistent pulmonary nodules on follow-up CT", Annual Meeting of the Radiological Society of North America, 2025.
    Abstract
  3. N. Antonissen, D. Peeters, B. Obreja, R. Dinnessen, Z. Saghir, M. Silva, U. Pastorino, E. Scholten, F. Mohamed Hoesein, R. Vliegenthart, H. Gietema, C. Schaefer-Prokop, M. Prokop and C. Jacobs, "Benchmarking radiologists and AI for indeterminate lung nodule malignancy risk estimation on screening CT: the LUNA25 Challenge", Annual Meeting of the Radiological Society of North America, 2025.
    Abstract
  4. D. Peeters, B. Obreja, N. Antonissen, R. Dinnessen, Z. Saghir, E. Scholten, R. Vliegenthart, M. Prokop and C. Jacobs, "Benchmarking of Artificial Intelligence and Radiologists for Lung Cancer Screening in CT: The LUNA25 Challenge", European Congress of Radiology, 2025.
    Abstract
  5. R. Dinnessen, K. Venkadesh, D. Peeters, H. Gietema, E. Scholten, C. Schaefer-Prokop and C. Jacobs, "External validation of an AI algorithm for pulmonary nodule malignancy risk estimation on a dataset of incidentally detected pulmonary nodules", European Congress of Radiology, 2024.
    Abstract
  6. D. Peeters, K. Venkadesh, R. Dinnessen, Z. Saghir, E. Scholten, R. Vliegenthart, M. Prokop and C. Jacobs, "Towards safe and reliable implementation of AI models for nodule malignancy estimation using distance-based out-of-distribution detection", Annual Meeting of the European Society of Thoracic Imaging, 2024.
    Abstract
  7. D. Peeters, N. Alves, K. Venkadesh, R. Dinnessen, Z. Saghir, E. Scholten, H. Huisman, C. Schaefer-Prokop, R. Vliegenthart, M. Prokop and C. Jacobs, "The effect of applying an uncertainty estimation method on the performance of a deep learning model for nodule malignancy risk estimation", European Congress of Radiology, 2023.
    Abstract

Master theses

  1. R. Geurtjens, D. Peeters and C. Jacobs, "Self-supervised Out-of-Distribution detection for medical imaging", Master thesis, 2023.
    Abstract