Raphaël Marée

me    Head of Cytomine ULiège R&D (Scientific Coordinator)

   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

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

Projects & Research Interests

Big 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; Rubens et al., 2019).
Cytomine web software is a "Google/OpenStreet Maps"-like rich internet application for remote visualization, annotation and automated analysis of high-resolution, multi-gigapixels images.
It is now actively used in various domains (including digital pathology, large-scale microscopy, and other fields) by various entities around the world.
We are continuously developing new software modules and algorithms for multimodal data sources (I'm co-lead of the Software workpackage of the COMULIS COST ACTION), benchmarking, and applications in various fields (see CYTOMINE research project page). In addition to our ongoing research & development at University of Liège, I'm co-founder of Cytomine SCRL FS, a not-for-profit cooperative company established to ease usage and improve sustainability of this open-source tool.

Open science & Commons

I'm very much in favor of basic principles of open science (open access, open data, open source, open hardware) and links to commons (shared resources), e.g. I published this review on Open Practices and Resources for Collaborative Digital Pathology (Frontiers in Medecine, 2019), and we designed BIAFLOWS, a Collaborative Framework to Reproducibly Deploy and Benchmark Bioimage Analysis Workflows (Cell Patterns, 2020; built on top of Cytomine).

I'm also involved in the BigPicture project (2021-2027, deputy lead of WP4) to establish the biggest open-access database of pathology images to accelerate the development of artificial intelligence in medicine.

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, deep learning methods, and transfer learning approaches, see e.g. our papers in CVPR-CVMI (2018) and IEEE-JBHI (2020). 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, ...).


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:

Software tools

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 encapsulated into Docker containers 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.

BIAFLOWS a collaborative framework to reproducibly deploy and benchmark bioimage analysis workflows (see demonstration server and documentation).

PiXiT was a Java software which implemented our CVPR 2005 image classification method. It is no longer available and this reimplementation in Python should be used:


Please contact me for Master final thesis proposals, internships, short-term scientific missions,... 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 using our Cytomine tool.


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 and discovering alternative places, taking pictures, and thinking about real utopias among other things.

University of Liege