Fall meeting 2016

Registration is free and includes coffee, lunch and drinks.
Upon registration, you will receive a link with which you can edit your details or unregister if necessary.

We also invite you to send in an abstract (maximum half a page A4, in English) about your work, for a 15-20 minute presentation. This can be previously published work, an ongoing project or an open question. You can submit your abstract via the registration form until the 21st of October.

Hourly Schedule


11:15 - 11:30
Thinsia Research project Heartbeat
Roland Sassen, Thinsia Research.
11:30 - 11:45
Spectral-Based Diagnosis of Cassava Crop Diseases with Leaf Images
Godliver Owomugisha, Friedrich Melchert, Ernest Mwebaze and Micheal Biehl, University of Groningen
11:45 - 12:00
The Inconsistency of Sequential Active Learning: An Empirical Investigation
Marco Loog and Yazhou Yang, Delft University of Technology & University of Copenhagen.
12:00 - 12:15
Radiogenomics classification of the 1p/19q status in presumed low-grade gliomas
Sebastian R. van der Voort, Renske Gahrmann, Martin J. van den Bent, Arnaud J.P.E. Vincent, Wiro J. Niessen, Marion Smits and Stefan Klein, Erasmus Medical Center.
12:15 - 14:45
Automatic detection of suspicious regions in whole slide imaging for patients with Barrett’s esophagus
Marit Lucas, Ilaria Jansen, Renan Sales Barros, Sybren L. Meijer, C. Dilara Savci Heijink, Onno J. de Boer, Anne-Fré Swager, Ton G. van Leeuwen, Daniel M. de Bruin and Henk A. Marquering, AMC
14:45 - 15:00
Aorta and Pulmonary Artery Segmentation with Optimal Surface Graph Cut in CT
Zahra Sedghi Gamechi, Andres M. Arias-Lorza, Daniel Bos, Jesper Pedersen, and Marleen de Bruijne, Erasmus MC & University of Copenhagen.
15:00 - 15:15
Automatic Propagation of 4D MRI Left Ventricle Endocardium Segmentation
Gabriela Belsley, Joao Tourais and Marcel Breeuwer, Eindhoven University of Technology & Philips Healthcare
15:15 - 15:30
The design of SuperElastix – a unifying framework for a wide range of image registration methodologies
Floris F. Berendsen, Kasper K. Marstal, Stefan Klein and Marius Staring, Leiden University Medical Center & Erasmus Medical Center.
15:30 - 15:45
Error estimation in deformable image registration using convolutional neural networks
Koen Eppenhof and Josien Pluim, Eindhoven University of Technology
Representation learning for cross-modality classification
Gijs van Tulder and Marleen de Bruijne, Erasmus Medical Center.


11 November 2016


09:30 - 17:30

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De Zwarte Doos, Eindhoven University of Technology
Den Dolech 2, 5612 AZ Eindhoven
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  • Diana Mateu
    Diana Mateu
    Technische Universität München

    Machine Learning for interactive histopathology quantification and multi-modal registration

    In the last years, big efforts have been deployed towards the use of machine learning algorithms to help analyzing medical images. In focus have been supervised methods applied to the problems of automatic segmentation (of cells, organs, etc) and classification or grading of diseases. However, in biomedical applications these efforts have been confronted with two specific challenges: on the one side, the limited limited access to expert labelled data and the other, the requirement of including an expert in the loop.

    In this talk, I will present two medical imaging problems, histopathology quantification and multi-modal image registration, and discuss the strategies we have developed to come around with the limitations above. First, In the histopathology case we propose an interactive domain adaption method to update random forests predictions with expert feedback. Second, we have recently addressed the problem of multi-modal registration through learning, were our solution relies on data augmentation and the incorporation of learned random forest predictions within a conventional optimization approach.

  • Joaquin Vanschoren
    Joaquin Vanschoren

    Democratizing and Automating Machine Learning

    Machine Learning is enabling many modern innovations, and lies at the heart of many empirical, data-driven sciences. Still, building machine learning systems remains something of an art, from gathering and transforming the right data to selecting and finetuning the most fitting modeling techniques. This makes it harder for students to study and succeed in the field, and causes (data) scientists to spend a lot of time on trial and error, and possibly settle for suboptimal results.

    OpenML is an open science platform for machine learning, allowing anyone to easily share data sets, code, and experiments, and collaborate with people all over the world to build better models. It shows, for any known data set, which are the best models, who built them, and how to reproduce and reuse them in different ways. It is readily integrated into several machine learning environments, so that you can share results with the touch of a button or a line of code. As such, it enables large-scale, real-time collaboration, allowing anyone to explore, build on, and contribute to the combined knowledge of the field.

    Ultimately, this provides a wealth of information for a novel, data-driven approach to machine learning, where we learn from millions of previous experiments to either assist people while analyzing data (e.g., which modeling techniques will likely work well and why), or automate the process altogether.

  • Julian de Wit
    Julian de Wit
    freelance machine learning specialist

    Diagnosing Heart Diseases with Deep Neural Networks

    In the beginning of 2016 Kaggle hosted the Second Annual Data Science Bowl challenge (https://www.kaggle.com/c/second-annual-data-science-bowl). The goal was to build a solution for automated volume estimation of the left ventricle based on MRI scans of patients. Using these volumes in a time sequence, doctors can detect various heart conditions. Nowadays measuring the volumes is still done manually by cardiac specialists. It is a very labor intensive job and therefore an automated solution would mean a big breakthrough.

    700 teams submitted their algorithms. This talk will discuss the best strategies which were all based on state of the art deep neural network architectures. The results were so impressive that projects are now being set up to use the solutions in a real clinical setting.

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