Publications of Mathias Prokop

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2024

Papers in international journals

  1. D. Zhong, G. Sidorenkov, C. Jacobs, P. de Jong, H. Gietema, R. Stadhouders, K. Nackaerts, J. Aerts, M. Prokop, H. Groen, G. de Bock, R. Vliegenthart, M. Heuvelmans and S. Atzen, "Lung Nodule Management in Low-Dose CT Screening for Lung Cancer: Lessons from the NELSON Trial", Radiology, 2024;313.
    Abstract DOI PMID
  2. S. Linden, L. Stam, R. Aquarius, A. Hering, C. de Korte, M. Prokop, H. Boogaarts, F. Meijer and L. Oostveen, "Feasibility of capturing vessel expansion with 4D-CTA: Phantom study to determine reproducibility, spatial and temporal resolution", Medical Physics, 2024;51:7171-7179.
    Abstract DOI PMID
  3. S. Schalekamp, K. van Leeuwen, E. Calli, K. Murphy, M. Rutten, B. Geurts, L. Peters-Bax, B. van Ginneken and M. Prokop, "Performance of AI to exclude normal chest radiographs to reduce radiologists' workload", European Radiology, 2024.
    Abstract DOI PMID
  4. 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

Preprints

  1. M. de Grauw, E. Scholten, E. Smit, M. Rutten, M. Prokop, B. van Ginneken and A. Hering, "The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomography", arXiv:2406.05231, 2024.
    Abstract DOI PMID arXiv
  2. H. Häntze, L. Xu, F. Dorfner, L. Donle, D. Truhn, H. Aerts, M. Prokop, B. van Ginneken, A. Hering, L. Adams and K. Bressem, "MRSegmentator: Robust Multi-Modality Segmentation of 40 Classes in MRI and CT Sequences", arXiv:2405.06463, 2024.
    Abstract DOI arXiv

Abstracts

  1. N. Antonissen, K. Venkadesh, H. Gietema, R. Vliegenthart, Z. Saghir, E. Scholten, M. Prokop, C. Schaefer-Prokop and C. Jacobs, "Retrospective validation and comparison of deep learning based risk thresholds versus growth-centric protocols in pulmonary nodule assessment in screening", Annual Meeting of the European Society of Thoracic Imaging, 2024.
    Abstract
  2. B. Obreja, K. Venkadesh, W. Hendrix, Z. Saghir, M. Prokop and C. Jacobs, "Deep Learning for estimating pulmonary nodule malignancy risk: How much data does AI need to reach radiologist level performance?", European Congress of Radiology, 2024.
    Abstract
  3. F. van der Graaf, N. Antonissen, Z. Saghir, M. Prokop and C. Jacobs, "External validation of the Sybil risk model as a tool to identify low-risk individuals eligible for biennial lung cancer screening", European Congress of Radiology, 2024.
    Abstract
  4. 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
  5. F. van der Graaf, N. Antonissen, E. Scholten, M. Prokop and C. Jacobs, "Assessing the agreement between privacy-preserving Llama model and human experts when labelling radiology reports for specific significant incidental findings in lung cancer screening", Annual Meeting of the European Society of Thoracic Imaging, 2024.
    Abstract

PhD theses

  1. S. van de Leemput, "Efficient neural network training for 4D-CTA stroke imaging", PhD thesis, 2024.
    Abstract Url
  2. K. Venkadesh, "AI for lung cancer screening", PhD thesis, 2024.
    Abstract Url
  3. G. Humpire-Mamani, "Deep Learning for Localization and Segmentation in Thorax Abdomen CT", PhD thesis, 2024.
    Abstract Url