Spring meeting NVPHBV 2016
We will hold the next Spring meeting 2016 on Friday 20th May 2016 at Erasmus MC in Rotterdam. Please mark your calendars!
Here is what you can expect from the meeting:
- Meet fellow practitioners of pattern recognition and image processing
- Present your own work
- Invited speakers and panel discussions (will be announced shortly)
- Lunch and drinks
- 09:40 - 10:00
- A Stochastic Quasi-Newton Method for Non-rigid Image Registration
- Yuchuan Qiao, Zhuo Sun, Boudewijn P.F. Lelieveldt, Marius Staring, LUMC
- 10:00 - 10:20
- Hands-Free Interactive Segmentation of Medical Volumes
- Florian Dubost, Erasmus MC & Lois Peter, Technical University Munich
- 10:20 - 10:40
- Anomaly detection in radiological images using deep learning
- Ioannis Katramados, COSMONiO
- 11:00 - 12:00
- Surgical data science for the operating room of the future
- 13:30 - 14:30
- Structured prediction with weak feedback from interaction logs
- 14:50 - 15:10
- Target Contrastive Estimator for Robust Domain Adaptation
- Wouter Kouw & Marco Loog, Delft University of Technology
- 15:10 - 15:30
- Active Learning Using Uncertainty Information
- Yazhou Yang & Marco Loog, Delft University of Technology
- 15:50 - 16:10
- Geometric connectivity analysis in curvilinear images based on the data-driven edge co-occurrences
- S. Abbasi-Sureshjani, J. Zhang, G. Sanguinetti, R. Duits, B. ter Haar Romeny, Eindhoven University of Technology
- 16:10 - 16:30
- Augmented Reality: from ARToolKit to HoloLens
- Ir. Lex van der Sluijs & Dr. John Schavemaker, TWNKLS
- 16:30 - 17:15
- Your own pattern recognition / image processing company?
- with COSMONiO, de tijdelijke expert, Quantib and TWNKLS
Lena Maier-HeinGerman Cancer Research Center (DKFZ)
Surgical data science for the operating room of the future
Despite spectacular advances in the field of medical imaging in the past decades, early cancer diagnosis and precise tumor therapy remain major healthcare challenges with high socioeconomic importance. Surgical data science is an emerging scientific discipline with the objective of improving the safety, quality, effectiveness, and efficiency of surgical care by means of data acquisition, modeling, and analysis. One key goal is to support physicians throughout the entire process of disease diagnosis, therapy and follow-up with the right information at the right time. To achieve this, we propose integration and advancement of methods from the research fields of machine learning, semantic modelling, medical image processing and biophotonics. The talk will highlight some of our recent contributions in this context with a particular focus on multispectral image analysis for cancer detection and crowdsourcing-based large-scale image annotation for context-aware guidance in tumor therapy.
Structured prediction with weak feedback from interaction logs
Log data is one of the most ubiquitous forms of data available, as it can be recorded from a variety of systems (e.g., search engine, ad placement engine, image segmenter) at little cost. The interaction logs of such systems typically contain a record of the input to the system (e.g., image), the prediction made by the system (e.g., segmentation of image) and the feedback provided by the user (e.g., user accepted/rejected this segmentation). This feedback, however, provides only partial-information feedback — aka “bandit feedback” — limited to the particular prediction shown by the system. The loss of all other possible predictions is not observed. This is fundamentally different from conventional supervised learning, where “correct” predictions (e.g., correct segmentation) together with a loss function provide full-information feedback.
In this talk, I will explore approaches and methods for batch learning from logged bandit feedback (BLBF). Unlike the well-explored problem of online learning with bandit feedback, batch learning with bandit feedback does not require interactive experimental control of the underlying system, but merely exploits logged interaction data collected in the past. The talk presents a new inductive principle for BLBF, new counterfactual risk estimators, and a new method for structured output prediction with BLBF. Joint work with Adith Swaminathan.