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August 25-28, 2020, Machine Learning & Knowledge Extraction, LNCS 12279 published !

Our Lecture Notes in Computer Sciene LNCS 12279 of our CD-MAKE Machine Learning & Knowledge Extraction conference   have been published and are available online via our conference homepage:

https://cd-make.net/proceedings

Content at a glance:

Explainable Artificial Intelligence: Concepts, Applications, Research
Challenges and Visions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Luca Longo, Randy Goebel, Freddy Lecue, Peter Kieseberg,
and Andreas Holzinger
The Explanation Game: Explaining Machine Learning Models
Using Shapley Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Luke Merrick and Ankur Taly
Back to the Feature: A Neural-Symbolic Perspective on Explainable AI. . . . . 39
Andrea Campagner and Federico Cabitza
Explain Graph Neural Networks to Understand Weighted Graph
Features in Node Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Xiaoxiao Li and João Saúde
Explainable Reinforcement Learning: A Survey . . . . . . . . . . . . . . . . . . . . . 77
Erika Puiutta and Eric M. S. P. Veith
A Projected Stochastic Gradient Algorithm for Estimating Shapley Value
Applied in Attribute Importance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
Grah Simon and Thouvenot Vincent
Explaining Predictive Models with Mixed Features Using Shapley Values
and Conditional Inference Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
Annabelle Redelmeier, Martin Jullum, and Kjersti Aas
Explainable Deep Learning for Fault Prognostics in Complex Systems:
A Particle Accelerator Use-Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
Lukas Felsberger, Andrea Apollonio, Thomas Cartier-Michaud,
Andreas M
üller, Benjamin Todd, and Dieter Kranzlmüller
eXDiL: A Tool for Classifying and eXplaining Hospital Discharge Letters. . . 159
Fabio Mercorio, Mario Mezzanzanica, and Andrea Seveso
Cooperation Between Data Analysts and Medical Experts: A Case Study. . . . 173
Judita Rokošná, František Babič, Ljiljana Trtica Majnarić,
and L
udmila Pusztová
A Study on the Fusion of Pixels and Patient Metadata in CNN-Based
Classification of Skin Lesion Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191
Fabrizio Nunnari, Chirag Bhuvaneshwara,
Abraham Obinwanne Ezema, and Daniel Sonntag
The European Legal Framework for Medical AI . . . . . . . . . . . . . . . . . . . . . 209
David Schneeberger, Karl Stöger, and Andreas Holzinger
An Efficient Method for Mining Informative Association Rules
in Knowledge Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227
Parfait Bemarisika and André Totohasina
Interpretation of SVM Using Data Mining Technique to Extract Syllogistic
Rules: Exploring the Notion of Explainable AI in Diagnosing CAD . . . . . . . 249
Sanjay Sekar Samuel, Nik Nailah Binti Abdullah, and Anil Raj
Non-local Second-Order Attention Network for Single Image
Super Resolution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267
Jiawen Lyn and Sen Yan
ML-ModelExplorer: An Explorative Model-Agnostic Approach to Evaluate
and Compare Multi-class Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281
Andreas Theissler, Simon Vollert, Patrick Benz, Laurentius A. Meerhoff,
and Marc Fernandes
Subverting Network Intrusion Detection: Crafting Adversarial Examples
Accounting for Domain-Specific Constraints. . . . . . . . . . . . . . . . . . . . . . . . 301
Martin Teuffenbach, Ewa Piatkowska, and Paul Smith
Scenario-Based Requirements Elicitation for User-Centric Explainable AI:
A Case in Fraud Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321
Douglas Cirqueira, Dietmar Nedbal, Markus Helfert,
and Marija Bezbradica
On-the-fly Black-Box Probably Approximately Correct Checking
of Recurrent Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343
Franz Mayr, Ramiro Visca, and Sergio Yovine
Active Learning for Auditory Hierarchy. . . . . . . . . . . . . . . . . . . . . . . . . . . 365
William Coleman, Charlie Cullen, Ming Yan, and Sarah Jane Delany
Improving Short Text Classification Through Global
Augmentation Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385
Vukosi Marivate and Tshephisho Sefara
Interpretable Topic Extraction and Word Embedding Learning
Using Row-Stochastic DEDICOM. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401
Lars Hillebrand, David Biesner, Christian Bauckhage,
and Rafet Sifa
A Clustering Backed Deep Learning Approach for Document
Layout Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423
Rhys Agombar, Max Luebbering, and Rafet Sifa
Calibrating Human-AI Collaboration: Impact of Risk, Ambiguity
and Transparency on Algorithmic Bias . . . . . . . . . . . . . . . . . . . . . . . . . . . 431
Philipp Schmidt and Felix Biessmann
Applying AI in Practice: Key Challenges and Lessons Learned. . . . . . . . . . . 451
Lukas Fischer, Lisa Ehrlinger, Verena Geist, Rudolf Ramler,
Florian Sobieczky, Werner Zellinger, and Bernhard Moser
Function Space Pooling for Graph Convolutional Networks . . . . . . . . . . . . . 473
Padraig Corcoran
Analysis of Optical Brain Signals Using Connectivity Graph Networks . . . . . 485
Marco Antonio Pinto-Orellana and Hugo L. Hammer
Property-Based Testing for Parameter Learning of Probabilistic
Graphical Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499
Anna Saranti, Behnam Taraghi, Martin Ebner, and Andreas Holzinger
An Ensemble Interpretable Machine Learning Scheme for Securing
Data Quality at the Edge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 517
Anna Karanika, Panagiotis Oikonomou, Kostas Kolomvatsos,
and Christos Anagnostopoulos
Inter-space Machine Learning in Smart Environments . . . . . . . . . . . . . . . . . 535
Amin Anjomshoaa and Edward Curry

The International Cross Domain Conference for MAchine Learning & Knowledge Extraction (CD-MAKE) is a joint effort of IFIP TC 5 (IT), TC 12 (Artificial Intelligence), IFIP WG 8.4 (E-Business), IFIP WG 8.9 (Information Systems), and IFIP WG 12.9 (Computational Intelligence) and is held in conjunction with the International Conference on Availability, Reliability and Security (ARES), see: 

https://www.ares-conference.eu/

The 4th conference is organized at the University College Dublin, Ireland and held as a virtual event, due to the Corona pandemic. A few words about the International Federation for Information Processing (IFIP):

IFIP is the leading multi-national, non-governmental, apolitical organization in Information and Communications Technologies and Computer Sciences, is recognized by the United Nations (UN), and was established in the year 1960 under the auspices of the UNESCO as an outcome of the first World Computer Congress held in Paris in
1959.

 

AI and Machine Learning for Digital Pathology

Artificial-Intelligence-and-Machine-Learning-for-Digital-Pathology

The Springer Lecture Notes in Artificial Intelligence LNAI 12090 have been published and are available online.

Ten Commandments for Human-AI interaction – Which are the most important?

In the following we present “10 Commandments for human-AI interaction” and ask our colleagues from the international AI/ML-community to comment on these. We will collect the results and present it openly to the international research community.

6th June 2019, 10:00 – 16:00 Graz/Austria Symposium AI/Machine Learning for Digital Pathology

On June, 6, 2019, 10:00-16:00 we organize in Graz/Austria a small Symposium on AI/Machine Learning for Digital Pathology

Miniconf Thursday, 20th December 2018: Raphaël Marée

Raphaël MARÉE  from the Montefiori Institute, Unviersity of Liege will visit us in week 51 and give a lecture on

Open and Collaborative Digital Pathology using Cytomine

When: Thursday, 20th December, 2018, at 10:00
Where: BBMRI Conference Room (joint invitation of BBMRI, ADOPT and HCI-KDD)
Address: Neue Stiftingtalstrasse 2/B/6, A-8010 Graz, Austria

Download pdf, 72kB

Abstract:

In this talk Raphael Maree will present the past, present, and future of Cytomine.
Cytomine [1], [2]  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.

Short Bio:

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.

[1]       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.

[2] https://www.cytomine.org 

Google Scholar Profile of Raphael Maree:
https://scholar.google.com/citations?user=qG66mF8AAAAJ&hl=en

Homepage of Raphael Maree:
https://www.montefiore.ulg.ac.be/~maree/

How different are Cats vs. Cells in Histopathology?

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:
https://giphy.com/gifs/microscope-fluorescence-mitosis-2G5llPaffwvio

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