The Springer Lecture Notes in Artificial Intelligence LNAI 12090 have been published and are available online.
Welcome to our XXAI ICML 2020 workshop: extending explainable ai beyond deep models and classifiers
Accepted Papers will be published in the Springer/Nature Lecture Notes in Computer Science Volume “Cross Domain Conference for Machine Learning and Knowlege Extraction” (CD-MAKE 2020)
This project will create an openQKD testbed for quantum communication which is highly relevant for future AI and machine learning
On June, 6, 2019, 10:00-16:00 we organize in Graz/Austria a small Symposium on AI/Machine Learning for Digital Pathology
Open and Collaborative Digital Pathology using Cytomine
In this talk Raphael Maree will present the past, present, and future of Cytomine.
Cytomine ,  is an open-source software, continuously developed since 2010. It is based on modern web and distributed software development methodologies and machine learning, i.e. deep learning. It provides remote and collaborative features so that users can readily and securely share their large-scale imaging data worldwide. It relies on data models that allow to easily organize and semantically annotate imaging datasets in a standardized way (e.g. to build pathology atlases for training courses or ground-truth datasets for machine learning). It efficiently supports digital slides produced by most scanner vendors. It provides mechanisms to proofread and share image quantifications produced by machine/deep learning-based algorithms. Cytomine can be used free of charge and it is distributed under a permissive license. It has been installed at various institutes worldwide and it is used by thousands of users in research and educational settings.
Recent research and developments will be presented such as our new web user interfaces and new modules for multimodal and multispectral data (Proteomics Clin Appl, 2019), object recognition in histology and cytology using deep transfer learning (CVMI 2018), user behavior analytics in educational settings (ECDP 2018), as well as our new reproducible architecture to benchmark bioimage analysis workflows.
Raphaël Marée received the PhD degree in computer science in 2005 from the University of Liège, Belgium, where he is now working at the Montefiore EE&CS Institute (https://www.montefiore.ulg.ac.be/~maree/). In 2010 he initiated the CYTOMINE research project (https://uliege.cytomine.org/), and since 2017 he is also co-founder of the not-for-profit Cytomine cooperative (https://cytomine.coop). His research interests are in the broad area of machine learning, computer vision techniques, and web-based software development, with specific focus on their applications on big imaging data such as in digital pathology and life science research, while following open science principles.
 Raphaël Marée, Loïc Rollus, Benjamin Stévens, Renaud Hoyoux, Gilles Louppe, Rémy Vandaele, Jean-Michel Begon, Philipp Kainz, Pierre Geurts & Louis Wehenkel 2016. Collaborative analysis of multi-gigapixel imaging data using Cytomine. Bioinformatics, 32, (9), 1395-1401, doi:10.1093/bioinformatics/btw013.
Google Scholar Profile of Raphael Maree:
Homepage of Raphael Maree:
An awesome question stated in an article by Michael BEREKET and Thao NGUYEN (Febuary 7, 2018) brings it straight to the point: Deep learning has revolutionized the field of computer vision. So why are pathologists still spending their time looking at cells through microscopes?
The most famous machine learning experiments have been done with recognizing cats (see the video by Peter Norvig) – and the question is relevant, how different are these cats from the cells in histopathology?
Machine Learning, and in particular deep learning, has reached a human-level in certain tasks, particularly in image classification. Interestingly, in the field of pathology these methods are not so ubiqutiously used currently. A valid question indeed is: Why do human pathologists spend so much time with visual inspection? Of course we restrict this debate on routine tasks!
This excellent article is worthwhile giving a read:
Stanford AI for healthcare: How different are cats from cells
Source of the animated gif above:
Yoshua BENGIO from the Canadian Institute for Advanced Research (CIFAR) emphasized during his workshop talk entitled “towards disentangling underlying explanatory factors” (cool title) at the ICML 2018 in Stockholm, that the key for success in AI/machine learning is to understand the explanatory/causal factors and mechanisms. This means generalizing beyond identical independent data (i.i.d.); current machine learning theories are strongly dependent on this iid assumption, but applications in the real-world (we see this in the medical domain!) often require learning and generalizing in areas simply not seen during the training epoch. Humans interestingly are able to protect themselves in such situations, even in situations which they have never seen before. See Yoshua BENGIO’s awesome talk here:
and here a longer talk (1:17:04) at Microsoft Research Redmond on January, 22, 2018 – awesome – enjoy the talk, I recommend it cordially to all my students!