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:
Recent advances in automatic machine learning (aML) allow solving problems without any human intervention, which is excellent in certain domains, e.g. in autonomous cars, where we want to exclude the human from the loop and want fully automatic learning. However, sometimes a human-in-the-loop can be beneficial – particularly in solving computationally hard problems. We provide new experimental insights  on how we can improve computational intelligence by complementing it with human intelligence in an interactive machine learning approach (iML). For this purpose, an Ant Colony Optimization (ACO) framework was used, because this fosters multi-agent approaches with human agents in the loop. We propose unification between the human intelligence and interaction skills and the computational power of an artificial system. The ACO framework is used on a case study solving the Traveling Salesman Problem, because of its many practical implications, e.g. in the medical domain. We used ACO due to the fact that it is one of the best algorithms used in many applied intelligence problems. For the evaluation we used gamification, i.e. we implemented a snake-like game called Traveling Snakesman with the MAX–MIN Ant System (MMAS) in the background. We extended the MMAS–Algorithm in a way, that the human can directly interact and influence the ants. This is done by “traveling” with the snake across the graph. Each time the human travels over an ant, the current pheromone value of the edge is multiplied by 5. This manipulation has an impact on the ant’s behavior (the probability that this edge is taken by the ant increases). The results show that the humans performing one tour through the graphs have a significant impact on the shortest path found by the MMAS. Consequently, our experiment demonstrates that in our case human intelligence can positively influence machine intelligence. To the best of our knowledge this is the first study of this kind and it is a wonderful experimental platform for explainable AI.
 Holzinger, A. et al. (2018). Interactive machine learning: experimental evidence for the human in the algorithmic loop. Springer/Nature: Applied Intelligence, doi:10.1007/s10489-018-1361-5.
Read the full article here:
“It’s time for AI to move out its adolescent, game-playing phase and take seriously the notions of quality and reliability.”
There is an interesting commentary with interviews by Don MONROE in the recent Communications of the ACM, November 2018, Volume 61, Number 11, Pages 11-13, doi: 10.1145/3276742 which emphasizes the importance of explainability and the need for effective human-computer interaction:
Artificial Intelligence (AI) systems are taking over a vast array of tasks that previously depended on human expertise and judgment (only). Often, however, the “reasoning” behind their actions is unclear, and can produce surprising errors or reinforce biased processes. One way to address this issue is to make AI “explainable” to humans—for example, designers who can improve it or let users better know when to trust it. Although the best styles of explanation for different purposes are still being studied, they will profoundly shape how future AI is used.
Some explainable AI, or XAI, has long been familiar, as part of online recommender systems: book purchasers or movie viewers see suggestions for additional selections described as having certain similar attributes, or being chosen by similar users. The stakes are low, however, and occasional misfires are easily ignored, with or without these explanations.
“Considering the internal complexity of modern AI, it may seem unreasonable to hope for a human-scale explanation of its decision-making rationale”.
Read the full article here:
Cartoon no. 1838 from the xkcd  Web comic by Randall MUNROE  describes in a brilliant sarcastic way the state of the art in AI/machine learning today and shows us the current main problem directly. Of course you will always get results from one of your machine learning models. Just fill in your data and you will get results – any results. That’s easy. The main question remains open: “What if the results are wrong?” The central problem is to know at all that my results are wrong and to what degree. Do you know your error? Or do you just believe what you get? This can be ignored in some areas, desired in other areas, but in a safety critical domain, e.g. in the medical area, this is crucial . Here also the interactive machine learning approach can help to compensate or lower the generalization error through human intuition .
 Andreas Holzinger, Chris Biemann, Constantinos S. Pattichis & Douglas B. Kell 2017. What do we need to build explainable AI systems for the medical domain? arXiv:1712.09923. online available: https://arxiv.org/abs/1712.09923v1
 Andreas Holzinger 2016. Interactive Machine Learning for Health Informatics: When do we need the human-in-the-loop? Brain Informatics, 3, (2), 119-131, doi:10.1007/s40708-016-0042-6. online available, see:
There is also a discussion on the image above:
From October, 21-22, 2018, the project partners of the EU RIA 826078 FeatureCloud project (EUR 4,646,000,00) met at the Technische Universität München, Campus Weihenstephan. Starting from January, 1, 2019 the project partners will work jointly for 60 months on awesome topics around federated machine learning and explainability. The project’s ground-breaking novel cloud-AI infrastructure will only exchange learned representations (the feature parameters theta θ, hence the name “feature cloud”) which are anonymous by default. This approach is privacy by design or to be more precise: privacy by architecture. The highly interdisciplinary consortium, ranging from AI and machine learning experts to medical professionals covers all aspects of the value chain: assessment of cyber risks, legal considerations and international policies, development of state-of-the-art federated machine learning technology coupled to blockchaining and encompasing social issues and AI-ethics.
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 “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.) – and this is crucial for our domain in medcial AI, because current machine learning theories and models are strongly dependent on this iid assumption, but applications in the real-world (we see this in the medical domain every day!) 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. 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 of my students!