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

Hourly Schedule

Program

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
Speakers:
Lena Maier-Hein
13:30 - 14:30
Structured prediction with weak feedback from interaction logs
Speakers:
Thorsten Joachims
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-Hein
Lena Maier-Hein
German 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.
Thorsten Joachims
Thorsten Joachims
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.

Date

20 May 2016

Time

09:30 - 17:30

Location

Erasmus Medical Center, Rotterdam
‘s-Gravendijkwal 230, 3015 CE Rotterdam

Speakers

  • Lena Maier-Hein
    Lena Maier-Hein
    German 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.

  • Thorsten Joachims
    Thorsten Joachims

    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.

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