• 947 Citations
20112019
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Personal profile

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.

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Research Output 2011 2019

Approximation of a pipeline of unsupervised retina image analysis methods with a CNN

Heslinga, F., Pluim, J., Dasht Bozorg, B., Berendschot, T., Houben, A. J. H. M., Henry, R. M. A. & Veta, M., 1 Mar 2019, Image Processing: SPIE Medical Imaging, 2019, San Diego, California, United States. Angelini, E. D. & Landman, B. A. (eds.). Bellingham: SPIE, 7 p. 109491N. (Proceedings of SPIE; vol. 10949)

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Biomarkers
Image analysis
Pipelines
Neural networks
Medical problems

Automated clear cell renal carcinoma grade classification with prognostic significance

Tian, K., Rubadue, C. A., Lin, D. I., Veta, M., Pyle, M. E., Irshad, H. & Heng, Y. J., 1 Jan 2019, In : PLoS ONE. 14, 10, 16 p., e0222641

Research output: Contribution to journalArticleAcademicpeer-review

Open Access
File
kidney cells
Renal Cell Carcinoma
carcinoma
Atlases
Hazards

Automatic cardiac landmark localization by a recurrent neural network

van Zon, M., Veta, M. & Li, S., 1 Jan 2019, Medical Imaging 2019: Image Processing. Landman, B. A. & Angelini, E. D. (eds.). Bellingham: SPIE, 13 p. 1094916. (Proceedings of SPIE; vol. 10949)

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

landmarks
Recurrent neural networks
Mitral Valve
Magnetic Resonance Spectroscopy
Magnetic resonance

Convolutional neural networks for segmentation of the left atrium from gadolinium-enhancement MRI images

de Vente, C., Veta, M., Razeghi, O., Niederer, S., Pluim, J., Rhode, K. & Karim, R., 14 Feb 2019, Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges - 9th International Workshop, STACOM 2018, Held in Conjunction with MICCAI 2018, Revised Selected Papers. Li, S., McLeod, K., Young, A., Rhode, K., Pop, M., Zhao, J., Mansi, T. & Sermesant, M. (eds.). Cham: Springer, p. 348-356 9 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11395 LNCS)

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Gadolinium
Magnetic resonance
Convolution
Magnetic resonance imaging
Segmentation

Deep learning-based prediction of kinetic parameters from myocardial perfusion MRI

Scannell, C. M., Bosch, P. V. D., Chiribiri, A., Lee, J., Breeuwer, M. & Veta, M., 27 Jul 2019, In : arXiv. 4 p., 1907.11899v1

Research output: Contribution to journalArticleAcademicpeer-review

Open Access
File
Kinetic parameters
Magnetic resonance imaging
Parameter estimation
Markov processes
Sampling

Courses

Medical image analysis

1/09/13 → …

Course

Project Imaging - BIA

1/09/14 → …

Course

Student theses

Canonical correlation analysis on microbial and metabolite data of individuals with metabolic syndrome

Author: van der Stam, J., 19 Jan 2019

Supervisor: Hilbers, P. (Supervisor 1), van Riel, N. (Supervisor 2), Veta, M. (Supervisor 2), Levin, E. (External person) (External coach) & Nieuwdorp, M. (External person) (External coach)

Student thesis: Master

Causal candidate genes identification for familial hypercholesterolemia family based on whole-genome sequencing

Author: Wang, Q., 31 Aug 2017

Supervisor: Hilbers, P. (Supervisor 1), van Riel, N. (Supervisor 2), Hovingh, K. (External person) (External coach), Bosnacki, D. (Supervisor 2) & Veta, M. (Supervisor 2)

Student thesis: Master

Content based CT retrieval for pulmonary nodules: deep metric learning based feature extraction

Author: Aerts, T., 28 Feb 2019

Supervisor: Menkovski, V. (Supervisor 1), Holenderski, M. (Supervisor 2) & Veta, M. (Supervisor 2)

Student thesis: Master

File

CycleGAN for coronay vessel segmentation

Author: van den Bosch, P., 29 Aug 2019

Supervisor: Pluim, J. (Supervisor 1), Bovendeerd, P. (Supervisor 2), Veta, M. (Supervisor 2) & Oliván-Bescós, J. (External person) (External coach)

Student thesis: Master

Deep learning approaches for prostate cancer detection and grading in Bi-parametric MRI

Author: de Vente, C., 29 Aug 2019

Supervisor: Veta, M. (Supervisor 1), Vos, P. (External person) (External coach), van Riel, N. (Supervisor 2) & Pluim, J. (Supervisor 2)

Student thesis: Master