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Liliana Petrychenko

Showing results (1-10 of 7) with videos related to

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|March 5, 2025
Towards Case-based Interpretability for Medical Federated LearningLaura Latorre, Liliana Petrychenko, Regina Beets-Tan, et al.
European Radiology|December 31, 2024
Generalizability, robustness, and correction bias of segmentations of thoracic organs at risk in CT imagesCorentin Guérendel, Liliana Petrychenko, Kalina Chupetlovska, et al.
European Journal of Radiology Open|April 25, 2024
Reproducing RECIST lesion selection via machine learning: Insights into intra and inter-radiologist variationTeresa M Tareco Bucho, Liliana Petrychenko, Mohamed A Abdelatty, et al.
European Radiology|May 16, 2026
Quality over quantity: biopsy-anchored CT radiogenomics models outperform all-lesion training in a multi-tumour cohort despite a smaller sample sizeDiana Ivonne Rodríguez Sánchez, Julian Middelkoop, Thera Vanneste, et al.
European Radiology Experimental|March 12, 2026
Tumor morphology on CT radiomics is largely driven by the local anatomical environment, not the primary tumor typeSajjad Rostami, Corentin Guérendel, Marleen Soliman, et al.
Radiology|June 24, 2025
A Data-Centric Approach to Deep Learning for Brain Metastasis Analysis at MRILaurens Topff, Liliana Petrychenko, Neeraj Jain, et al.
The Lancet. Oncology|June 17, 2026
Development and validation of artificial intelligence-assisted volumetric response criteria in pleural mesothelioma (ARTIMES): a retrospective, multicohort, multicentre studyKevin B W Groot Lipman, Rianne Wittenberg, Mateus de Oliveira Taveira, et al.
Pageof 1

Showing results (1-10 of 7) with videos related to

Sort By:
Pageof 1
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|March 5, 2025
Towards Case-based Interpretability for Medical Federated LearningLaura Latorre, Liliana Petrychenko, Regina Beets-Tan, et al.
European Radiology|December 31, 2024
Generalizability, robustness, and correction bias of segmentations of thoracic organs at risk in CT imagesCorentin Guérendel, Liliana Petrychenko, Kalina Chupetlovska, et al.
European Journal of Radiology Open|April 25, 2024
Reproducing RECIST lesion selection via machine learning: Insights into intra and inter-radiologist variationTeresa M Tareco Bucho, Liliana Petrychenko, Mohamed A Abdelatty, et al.
European Radiology|May 16, 2026
Quality over quantity: biopsy-anchored CT radiogenomics models outperform all-lesion training in a multi-tumour cohort despite a smaller sample sizeDiana Ivonne Rodríguez Sánchez, Julian Middelkoop, Thera Vanneste, et al.
European Radiology Experimental|March 12, 2026
Tumor morphology on CT radiomics is largely driven by the local anatomical environment, not the primary tumor typeSajjad Rostami, Corentin Guérendel, Marleen Soliman, et al.
Radiology|June 24, 2025
A Data-Centric Approach to Deep Learning for Brain Metastasis Analysis at MRILaurens Topff, Liliana Petrychenko, Neeraj Jain, et al.
The Lancet. Oncology|June 17, 2026
Development and validation of artificial intelligence-assisted volumetric response criteria in pleural mesothelioma (ARTIMES): a retrospective, multicohort, multicentre studyKevin B W Groot Lipman, Rianne Wittenberg, Mateus de Oliveira Taveira, et al.
Pageof 1