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Mitko Veta

Showing results (1-10 of 67) with videos related to

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Medical Image Analysis|June 7, 2019
Corrigendum to "Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge" [Medical Image Analysis, 54 (2019) 111--121]Mitko Veta
European Heart Journal|May 17, 2021
Can automatic image analysis replace the pathologist in cardiac allograft rejection diagnosis?Mitko Veta, Paul J van Diest, Aryan Vink
IEEE Transactions on Bio-Medical Engineering|August 14, 2020
Intensity Augmentation to Improve Generalizability of Breast Segmentation Across Different MRI Scan ProtocolsLinde S Hesse, Grey Kuling, Mitko Veta, et al.
Journal of Pathology Informatics|July 17, 2013
Going fully digital: Perspective of a Dutch academic pathology labNikolas Stathonikos, Mitko Veta, André Huisman, et al.
IEEE Transactions on Bio-Medical Engineering|May 13, 2020
Deep Learning Regression for Prostate Cancer Detection and Grading in Bi-Parametric MRICoen de Vente, Pieter Vos, Matin Hosseinzadeh, et al.
Frontiers in Medicine|August 6, 2019
Learning Domain-Invariant Representations of Histological ImagesMaxime W Lafarge, Josien P W Pluim, Koen A J Eppenhof, et al.
IEEE Transactions on Bio-Medical Engineering|April 25, 2014
Breast cancer histopathology image analysis: a reviewMitko Veta, Josien P W Pluim, Paul J van Diest, et al.
Plos One|December 5, 2025
Beyond accuracy: Quantifying the reliability of multiple instance learning for whole slide image classificationHassan Keshvarikhojasteh, Marc Aubreville, Christof A Bertram, et al.
IEEE Transactions on Medical Imaging|November 22, 2019
Progressively Trained Convolutional Neural Networks for Deformable Image RegistrationKoen A J Eppenhof, Maxime W Lafarge, Mitko Veta, et al.
Translational Vision Science & Technology|September 5, 2020
Quantifying Graft Detachment after Descemet's Membrane Endothelial Keratoplasty with Deep Convolutional Neural NetworksFriso G Heslinga, Mark Alberti, Josien P W Pluim, et al.
Pageof 7

Showing results (1-10 of 67) with videos related to

Sort By:
Pageof 7
Medical Image Analysis|June 7, 2019
Corrigendum to "Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge" [Medical Image Analysis, 54 (2019) 111--121]Mitko Veta
European Heart Journal|May 17, 2021
Can automatic image analysis replace the pathologist in cardiac allograft rejection diagnosis?Mitko Veta, Paul J van Diest, Aryan Vink
IEEE Transactions on Bio-Medical Engineering|August 14, 2020
Intensity Augmentation to Improve Generalizability of Breast Segmentation Across Different MRI Scan ProtocolsLinde S Hesse, Grey Kuling, Mitko Veta, et al.
Journal of Pathology Informatics|July 17, 2013
Going fully digital: Perspective of a Dutch academic pathology labNikolas Stathonikos, Mitko Veta, André Huisman, et al.
IEEE Transactions on Bio-Medical Engineering|May 13, 2020
Deep Learning Regression for Prostate Cancer Detection and Grading in Bi-Parametric MRICoen de Vente, Pieter Vos, Matin Hosseinzadeh, et al.
Frontiers in Medicine|August 6, 2019
Learning Domain-Invariant Representations of Histological ImagesMaxime W Lafarge, Josien P W Pluim, Koen A J Eppenhof, et al.
IEEE Transactions on Bio-Medical Engineering|April 25, 2014
Breast cancer histopathology image analysis: a reviewMitko Veta, Josien P W Pluim, Paul J van Diest, et al.
Plos One|December 5, 2025
Beyond accuracy: Quantifying the reliability of multiple instance learning for whole slide image classificationHassan Keshvarikhojasteh, Marc Aubreville, Christof A Bertram, et al.
IEEE Transactions on Medical Imaging|November 22, 2019
Progressively Trained Convolutional Neural Networks for Deformable Image RegistrationKoen A J Eppenhof, Maxime W Lafarge, Mitko Veta, et al.
Translational Vision Science & Technology|September 5, 2020
Quantifying Graft Detachment after Descemet's Membrane Endothelial Keratoplasty with Deep Convolutional Neural NetworksFriso G Heslinga, Mark Alberti, Josien P W Pluim, et al.
Pageof 7