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Mart van Rijthoven

Showing results (1-10 of 6) with videos related to

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Medical Image Analysis|December 1, 2020
HookNet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide imagesMart van Rijthoven, Maschenka Balkenhol, Karina Siliņa, et al.
Nature Communications|May 15, 2026
Analysis of computational tumor-infiltrating lymphocytes in breast cancer from the results of the TIGER challengeMart van Rijthoven, Witali Aswolinskiy, Leslie Tessier, et al.
Journal of Medical Imaging (Bellingham, Wash.)|June 17, 2026
Methodological considerations for evaluating deep learning segmentation models in digital pathology whole-slide imagesArian Arab, Victor Garcia, Seyed Kahaki, et al.
Communications Medicine|January 5, 2024
Multi-resolution deep learning characterizes tertiary lymphoid structures and their prognostic relevance in solid tumorsMart van Rijthoven, Simon Obahor, Fabio Pagliarulo, et al.
Medical Image Analysis|September 3, 2019
Learning to detect lymphocytes in immunohistochemistry with deep learningZaneta Swiderska-Chadaj, Hans Pinckaers, Mart van Rijthoven, et al.
NPJ Digital Medicine|July 22, 2022
Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotationsNiccolò Marini, Stefano Marchesin, Sebastian Otálora, et al.
Pageof 1

Showing results (1-10 of 6) with videos related to

Sort By:
Pageof 1
Medical Image Analysis|December 1, 2020
HookNet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide imagesMart van Rijthoven, Maschenka Balkenhol, Karina Siliņa, et al.
Nature Communications|May 15, 2026
Analysis of computational tumor-infiltrating lymphocytes in breast cancer from the results of the TIGER challengeMart van Rijthoven, Witali Aswolinskiy, Leslie Tessier, et al.
Journal of Medical Imaging (Bellingham, Wash.)|June 17, 2026
Methodological considerations for evaluating deep learning segmentation models in digital pathology whole-slide imagesArian Arab, Victor Garcia, Seyed Kahaki, et al.
Communications Medicine|January 5, 2024
Multi-resolution deep learning characterizes tertiary lymphoid structures and their prognostic relevance in solid tumorsMart van Rijthoven, Simon Obahor, Fabio Pagliarulo, et al.
Medical Image Analysis|September 3, 2019
Learning to detect lymphocytes in immunohistochemistry with deep learningZaneta Swiderska-Chadaj, Hans Pinckaers, Mart van Rijthoven, et al.
NPJ Digital Medicine|July 22, 2022
Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotationsNiccolò Marini, Stefano Marchesin, Sebastian Otálora, et al.
Pageof 1