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.
Retinal microvascular biomarker extraction on fundus images from the Maastricht study using supervised deep learning(2019)
Detection of acini in histopathology slidesSPIE Medical Imaging 2019 (2019)
Approximation of a pipeline of unsupervised retina image analysis methods with a CNNSPIE Medical Imaging 2019 (2019)
Predicting breast tumor proliferation from whole-slide imagesMedical Image Analysis (2019)
Convolutional neural networks for segmentation of the left atrium from gadolinium-enhancement MRI images9th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018 (2019)
- Team challenge in medical imaging
- Project Imaging - BIA
- Medical image analysis
No ancillary activities