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.
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.
Corrigendum to “Predicting breast tumor proliferation from whole-slide imagesMedical Image Analysis (2019)
Learning domain-invariant representations of histological imagesFrontiers in Medicine (2019)
Retinal microvascular biomarker extraction on fundus images from the Maastricht study using supervised deep learning3rd Joint Meeting of the European Society for Microcirculation (ESM) and the European Vascular Biology Organization (EVBO) (2019)
Retinal microvascular biomarker extraction on fundus images from the Maastricht study using supervised deep learningJournal of Vascular Research (2019)
Detection of acini in histopathology slidesSPIE Medical Imaging 2019 (2019)