Spring meeting 2017

The NVPHBV Spring Meeting 2017 was held on Monday, 22 May 2017 in Leiden, the Netherlands. Location: van Steenis Building, Einsteinweg 2, 2333 CC Leiden, room F 104. The meeting was hosted by LUMC and LIACS.

Hourly Schedule

Program

09:00 - 09:30
Coffee
09:30 - 09:40
Opening and welcome
Bart ter Haar Romeny, president NVPHBV. Marius Staring, host LKEB-LUMC. Cor Veenman, host LIACS
09:40 - 10:10
Nearest Neighbor search of points in high dimensional space using binary vantage point features.
Sjaak Koomen, Prime Vision B.V.
10:10 - 10:40
Automatic artery/vein classification in retinal images
Fan Huang, Behdad Dashtbozorg, Bart ter Haar Romeny
10:40 - 11:00
Break
11:00 - 11:30
Development of a snapshot hyperspectral imager and automated interpretation algorithms for laparoscopy
Nanda van der Stap, Lejla Alic, Klamer Schutte
11:30 - 12:30
Keynote: The state-of-the-art and future of Intelligent Machines
Speakers:
Prof. dr. Max Welling
12:30 - 14:00
Lunch and Member meeting
14:00 - 14:30
Prediction of histopathological growth patterns in colorectal liver metastases using a Radiomics approach
14:30 - 15:30
Keynote: Mining electronic health records for computer-aided decision in ophthalmology
Speakers:
Gwenolé Quellec
15:30 - 16:00
Break
16:00 - 16:30
No Contact Full Body Tremor Measurements
S.L. Pintea, P.J.M.van_Schaik-Bank, J.C. van Gemert, J. Marinus
16:30 - 17:00
Discussion: Congratulations with your PhD, what’s next?
Speakers:
Léon Bemelmans
17:00 -
Drinks
Prof. dr. Max Welling
Prof. dr. Max Welling
Machine Learning, University of Amsterdam
Generalizing Convolutions for Deep Learning Arguably, most excitement about deep learning revolves around the performance of convolutional neural networks and their ability to automatically extract useful features from signals. In this talk I will present work from AMLAB where we generalize these convolutions. First we study convolutions on graphs and propose a simple new method to learn embeddings of graphs which are subsequently used for semi-supervised learning and link prediction. We discuss applications to recommender systems and knowledge graphs. Second we propose a new type of convolution on regular grids based on group transformations. This generalizes normal convolutions based on translations to larger groups including the rotation group. Both methods often result in significant improvements relative to the current state of the art. Joint work with Thomas Kipf, Rianne van den Berg and Taco Cohen.
Gwenolé Quellec
Gwenolé Quellec
Université de Bretagne Occidentale, Inserm, Brest, France
Mining electronic health records for computer-aided decision in ophthalmology Electronic health records are a major asset for the development of innovative computer-aided decision tools. Through the retrospective analysis of medical records sharing the same clinical interpretations, new decision rules can be mined. Typically, by comparing examination records from patients diagnosed with the same pathology, we can learn to detect that pathology in a new patient, and even to segment its clinical signs in new images. Similarly, by comparing surgeries with similar intraoperative complications, it may be possible to warn the surgeon ahead of time during a new surgery. However, because the most informative data in examination and surgical records are images and videos, the underlying data mining process can be quite challenging. Solutions based on multiple-instance learning and deep learning, two paradigms suitable for image and video mining, will be discussed in this talk. Examples will be given primarily for two ophthalmic applications: retinal pathology screening, using fundus photographs, and cataract surgery monitoring, using surgical microscope videos. Competitive results were achieved by the presented solutions. For instance, our latest algorithm for screening diabetic retinopathy is as good as the top ranking solution of Kaggle’s diabetic retinopathy detection challenge. But, unlike solutions of this challenge, which only provide predictions at the image level, our algorithm can also finely detect lesions in images: it even outperforms lesion detectors trained with manual segmentations. Electronic health record mining is not only convenient, in that it does not require manual segmentations, it is also accurate. 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.
Léon Bemelmans
Léon Bemelmans
VinciTech BV
Congratulations with your PhD, what’s next? The choice to go for and finalize your PhD reflects a certain feeling with academic subjects, thoroughness end endurance. Continuing the professional career in an academic research environment is not available or desirable for everyone. The most important alternative seems to be to work at large companies like Philips, ASML, to name only two. However, other options might be to work at smaller companies (Small and Medium Sized Enterprises, SME’s) or even to start your own company. The talk aims at giving some insights at the consequences for such a choice on aspects like depth of research, variety of tasks and challenges, relation to required skills and remuneration. These insights are based on the speaker’s experience in founding and running small technically oriented companies (previously Beltech BV, machine vision, 16 f.t.e. and currently VinciTech BV, robotics, 2 f.t.e.). The talk will be followed by a discussion with the audience.

Date

22 May 2017

Time

08:00 - 18:00

Location

Leiden University
Leiden University, Van Steenis Building Einsteinweg 2, 2333 CC Leiden

Speakers

  • Gwenolé Quellec
    Gwenolé Quellec
    Université de Bretagne Occidentale, Inserm, Brest, France

    Mining electronic health records for computer-aided decision in ophthalmology

    Electronic health records are a major asset for the development of innovative computer-aided decision tools. Through the retrospective analysis of medical records sharing the same clinical interpretations, new decision rules can be mined. Typically, by comparing examination records from patients diagnosed with the same pathology, we can learn to detect that pathology in a new patient, and even to segment its clinical signs in new images. Similarly, by comparing surgeries with similar intraoperative complications, it may be possible to warn the surgeon ahead of time during a new surgery. However, because the most informative data in examination and surgical records are images and videos, the underlying data mining process can be quite challenging. Solutions based on multiple-instance learning and deep learning, two paradigms suitable for image and video mining, will be discussed in this talk. Examples will be given primarily for two ophthalmic applications: retinal pathology screening, using fundus photographs, and cataract surgery monitoring, using surgical microscope videos. Competitive results were achieved by the presented solutions. For instance, our latest algorithm for screening diabetic retinopathy is as good as the top ranking solution of Kaggle’s diabetic retinopathy detection challenge. But, unlike solutions of this challenge, which only provide predictions at the image level, our algorithm can also finely detect lesions in images: it even outperforms lesion detectors trained with manual segmentations. Electronic health record mining is not only convenient, in that it does not require manual segmentations, it is also accurate.

    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.

  • Léon Bemelmans
    Léon Bemelmans
    VinciTech BV

    Congratulations with your PhD, what’s next?

    The choice to go for and finalize your PhD reflects a certain feeling with academic subjects, thoroughness end endurance. Continuing the professional career in an academic research environment is not available or desirable for everyone.
    The most important alternative seems to be to work at large companies like Philips, ASML, to name only two. However, other options might be to work at smaller companies (Small and Medium Sized Enterprises, SME’s) or even to start your own company.
    The talk aims at giving some insights at the consequences for such a choice on aspects like depth of research, variety of tasks and challenges, relation to required skills and remuneration.
    These insights are based on the speaker’s experience in founding and running small technically oriented companies (previously Beltech BV, machine vision, 16 f.t.e. and currently VinciTech BV, robotics, 2 f.t.e.). The talk will be followed by a discussion with the audience.

  • Prof. dr. Max Welling
    Prof. dr. Max Welling
    Machine Learning, University of Amsterdam

    Generalizing Convolutions for Deep Learning

    Arguably, most excitement about deep learning revolves around the performance of convolutional neural networks and their ability to automatically extract useful features from signals. In this talk I will present work from AMLAB where we generalize these convolutions. First we study convolutions on graphs and propose a simple new method to learn embeddings of graphs which are subsequently used for semi-supervised learning and link prediction. We discuss applications to recommender systems and knowledge graphs. Second we propose a new type of convolution on regular grids based on group transformations. This generalizes normal convolutions based on translations to larger groups including the rotation group. Both methods often result in significant improvements relative to the current state of the art.

    Joint work with Thomas Kipf, Rianne van den Berg and Taco Cohen.

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