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Ine Dirks
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2020 – today
- 2024
- [j2]Luuk H. Boulogne
, Julian Lorenz, Daniel Kienzle, Robin Schön, Katja Ludwig, Rainer Lienhart, Simon Jégou, Guang Li, Cong Chen, Qi Wang, Derik Shi, Mayug Maniparambil, Dominik Müller, Silvan Mertes, Niklas Schröter, Fabio Hellmann, Miriam Elia, Ine Dirks, Matías Nicolás Bossa, Abel Díaz Berenguer
The STOIC2021 COVID-19 AI challenge: Applying reusable training methodologies to private data. Medical Image Anal. 97: 103230 (2024) - 2023
- [i3]Luuk H. Boulogne, Julian Lorenz, Daniel Kienzle, Robin Schön, Katja Ludwig, Rainer Lienhart, Simon Jégou, Guang Li, Cong Chen, Qi Wang, Derik Shi, Mayug Maniparambil, Dominik Müller, Silvan Mertes, Niklas Schröter, Fabio Hellmann, Miriam Elia, Ine Dirks
The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data. CoRR abs/2306.10484 (2023) - 2022
- [j1]Ine Dirks
Computer-aided detection and segmentation of malignant melanoma lesions on whole-body 18F-FDG PET/CT using an interpretable deep learning approach. Comput. Methods Programs Biomed. 221: 106902 (2022) - 2020
- [c1]Ine Dirks
Automated threshold selection on whole-body 18F-FDG PET/CT for assessing tumor metabolic response. Image Processing 2020: 113131R - [i2]Sofie Tilborghs, Ine Dirks
Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients. CoRR abs/2007.15546 (2020) - [i1]Abel Díaz Berenguer
Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging. CoRR abs/2011.11719 (2020)
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last updated on 2024-12-10 21:42 CET by the dblp team
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