Mitko Veta
Department / Institute
Group
RESEARCH PROFILE
Mitko Veta is an assistant professor at the TU/e research group Medical Image Analysis, department of Biomedical Engineering. His research concerns the design, implementation and evaluation of image analysis methods for histopathology images and digital slides. Currently his focus is on the development and application of deep learning methods for medical image analysis.
The research aims to develop automatic quantitative histopathology image analysis algorithms that will increase the reproducibility and accuracy of pathology reporting and reduce the workload of pathologists. This will lead to better treatment planning for the patients and reduction of healthcare costs.
ACADEMIC BACKGROUND
MitkoVeta studied Electrical Engineering at the Ss. Cyril and Methodius University in Skopje (Macedonia) where he in 2009 received his Master's degree on Digital Signal Processing with a thesis on digital video classification. In 2010, he moved to the University Medical Center Utrecht (The Netherlands) to perform PhD research on the topic of automatic analysis of histopathology images. In 2014, he obtained his doctorate and started as a postdoctoral researcher at Eindhoven University of Technology (TU/e, the Netherlands). In 2016, he was appointed assistant professor with the TU/e research group Medical Image Analysis of the department of Biomedical Engineering.
Recent Publications
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Dennis M.J. van de Sande,Julian P. Merkofer,Sina Amirrajab,Mitko Veta,Ruud J.G. van Sloun,Maarten J. Versluis,Jacobus F.A. Jansen,Johan S. van den Brink,Marcel Breeuwer
A review of machine learning applications for the proton MR spectroscopy workflow
Magnetic Resonance in Medicine (2023) -
Rodrigo Pommot Berto,Hanna Bugler,Gabriel Dias,Mateus Oliveira,Lucas Ueda,Sergio Dertkigil,Paula D.P. Costa,Leticia Rittner,Julian P. Merkofer,Dennis M.J. van de Sande
Advancing GABA-edited MRS Research through a Reconstruction Challenge
(2023) -
Julian P. Merkofer,Dennis M.J. van de Sande,Sina Amirrajab,Gerhard S. Drenthen,Mitko Veta,Jacobus F.A. Jansen,Marcel Breeuwer,Ruud J.G. van Sloun
A Deep Learning Approach Utilizing Covariance Matrix Analysis for the ISBI Edited MRS Reconstruction Challenge
(2023) -
Dennis M.J. van de Sande,Julian P. Merkofer,Sina Amirrajab,Mitko Veta,Ruud J.G. van Sloun,Maarten J. Versluis,Jacobus F.A. Jansen,Johan S. van den Brink,Marcel Breeuwer
A Review of Machine Learning Applications for the Proton Magnetic Resonance Spectroscopy Workflow
arXiv (2023) -
Laurens S. ter Maat,Isabella A.J. van Duin,Sjoerd G. Elias,Tim Leiner,Joost J.C. Verhoeff,Eran R.A.N. Arntz,Max F. Troenokarso,Willeke A.M. Blokx,Ivana Isgum,Geraldine A. de Wit
CT radiomics compared to a clinical model for predicting checkpoint inhibitor treatment outcomes in patients with advanced melanoma
European Journal of Cancer (2023)
Ancillary Activities
No ancillary activities