Publications of Dré Peeters

2024

Papers in international journals

1. 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.
   Abstract DOI PMID

Abstracts

1. 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
2. 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

2023

Papers in conference proceedings

1. 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. 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