Speakers

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  Lena Maier-Hein German Cancer Research Center (DKFZ)
  

  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.

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  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.