Raphaël Marée

me    PhD in computer science (since 2005)
   E-mail: Raphael.Maree@uliege.be
   LinkedIn - Google Scholar - Twitter -
   Tél: (+32) 4 366 26 44
       Montefiore Institute (B28)
       Quartier Polytech 1
       10, Allée de la découverte
       University of Liège
       4000 Liège
University of Liege FEDER FSE

Curriculum vitae - Projects & Research Interests - Publications - Fundings - Software - Conference Calendar - Students - Misc

Projects & Research Interests

My personal research interests are in large-scale image informatics, machine learning, computer vision, big data, and open science.

Image informatics

In 2010, I initiated the CYTOMINE research project which lead to the development of the CYTOMINE open-source software platform (Marée et al., Bioinformatics 2016), a "Google/OpenStreet Maps"-like rich internet application for remote visualization, annotation and automated analysis of high-resolution, multi-gigapixels images.
It is now used in various domains (including digital pathology, large-scale microscopy, and other fields) by various entities around the world.
We are now focusing on the design of new software modules and algorithms for multispectral/multimodal data sources, benchmarking, and user behavior analytics (see CYTOMINE research project page).

In addition to ongoing research at University of Liège, we created Cytomine SCRL FS, a not-for-profit cooperative company to provide services on top of the open-source software.

cytomine applications

Machine/Deep learning for computer vision

Since 2003 we develop general-purpose methods for the recognition of various types of images that share some visual regularities, without relying on too strong assumptions about patterns to recognize and acquisition conditions, and without having to rely on domain experts to design specific features.
This book chapter (2013) summarizes our previous work where we combined ensemble of randomized decision trees with random extraction of subwindows (square patches) described by their raw pixel values. To assess versatility of our methods, we often perform large-scale empirical studies e.g. in Pattern Recognition Letters (2016) for image classification, or Nature Scientific Reports (2018) in for interest point (landmark) detection. More recently, we are trying to combine ideas from tree-based methods with deep learning methods, see e.g. our CVPR-CVMI paper (2018). Since 2016 these algorithms are integrated into the aforementioned CYTOMINE open-source software.


Between January 2005 and October 2014, I was the GIGA Bioinformatics platform manager. We offered software development and data analysis services to academic (within the GIGA research center and beyond) as well as to industrial researchers. Services included classification of biological/biomedical data (SELDI mass spectra, microarrays, clinical databases, ...) obtained from various medical instrumentation based on machine learning methods. This activity lead to co-authorship of several journal papers (in Journal of Immunology, Proteomics, Annals of the rheumatic diseases, ...) in various application domains (inflammatory diseases, cancer, ...).

Open science

I'm very much in favor of basic principles of open science (open access, open data, open source, open hardware) although I still have much to learn about it.


To access all my publications on the institutional repository (ORBI), please go here.

Our citations according to Google Scholar are here.

Most of my publications are directly available, others (with publisher's constraints, symbolized by the padlock) are also available after filling a simple request form:


CYTOMINE is a "Google Maps"-like rich internet application for remote visualization, collaborative and semantic annotation and automated analysis of high-resolution images. It is released under an open-source software license and an official release is maintained by Cytomine cooperative. The software also includes data mining modules in Python with our latest algorithm developments for image classification, semantic segmentation, and landmark detection, and a Java module for content-based retrieval. It can be run on big servers for large-scale studies, or on laptops for small-scale works.

PiXiT, a Java software which implements our CVPR 2005 image classification method is available upon request for evaluation and non-commercial purposes, in collaboration with PEPITe. Newer extensions and improvments are not included in this version.

Conference Calendar

For years, for personal usage I'm trying to maintain a unofficial conference calendar (.ics file) in the fields of computer vision, bioimage informatics, machine learning, biomedical imaging.

In the field of computer vision, the conference listing from USC is far more complete.


Please contact me for Master final thesis proposals, traineeships, ... in bioimage informatics or machine learning for computer vision. Especially if you are interested to be involved in open-source software development using machine learning and modern web technologies and their applications to real-world (big) image data.


I listen very much to music (mostly ambient/electronica/idm/modern classical/fields recordings) like in these selections.
I co-organized the Panoptica festival with friends and I'm now involved in the Supervue festival.
I also enjoy reading (society essays and "independent" comics), eating delicious vegetarian food, traveling, taking pictures, thinking, ...

Seoul(Korea) San Diego(San Diego) San Diego(Beijing) Tokyo(Tokyo-Kyoto) Lisbon (Lisbon) Belle-Île(Brittany)

University of Liege FEDER FSE