Speakers

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  Joaquin Vanschoren TU/e
  

  Joaquin Vanschoren

  TU/e

  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.

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  Diana Mateu Technische Universität München
  

  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.

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  Julian de Wit freelance machine learning specialist
  

  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.