Best Poster Award MIDL 2019
Congratulations to Thomas de Bel for winning the Best Poster Award at the second edition of the International Conference on Medical Imaging with Deep Learning held in London this week.
News
Final meeting AMI-project.
The final meeting of the AMI-project took place last week. The AMI-project was a close collaborative project between the Diagnostic Image Analysis Group and the Fraunhofer Institute for Digital Medicine MEVIS. With the development of a generic platform for automatic medical image analysis, the project was a succes.
Mitotic counting via deep learning and manual assessment is not prognostic in TNBC.
Maschenka Balkenhol et al assessed the prognostic value of absolute mitotic counts for triple negative breast cancer, using both deep learning and manual procedures. Yesterday their work appeared online in Cellular Oncology.
Nathan Kaufman timely topics lecture for Jeroen van der Laak at USCAP 2019
Jeroen van der Laak was honored as Nathan Kaufman timely topics lecturer at the 108th annual meeting of the United States and Canadian Academy of Pathology (USCAP). This lecture is regarded as a great honor within the USCAP sphere.
Computer aided quantification of TSR as independent prognosticator in rectal cancer
Computational pathology group member Oscar Geessink and colleagues investigated the potential of computer-aided quantification of intratumoral stroma in rectal cancer whole-slide images. This month, their work has been accepted for publication by Celullar Oncology.
Kick-off H2020 project ExaMode
20 European researchers gathered last week at the Techno-pôle in Sierre, Switserland to kick-off the European H2020 project ExaMode. The objective of the project is to develop new prototypes for processing large volumes of medical data on exascale computing facilities.
EU H2020 and KWF grant for Francesco Ciompi and Jeroen van der Laak
Last month, Computational pathology group-members Francesco Ciompi and Jeroen van der Laak have been awarded 2 grants for the EXAMODE and PROACTING project. Both projects have a focus on cancer research based on deep learning techniques.
Automatic mitosis detection in breast cancer tissue sections
Counting of mitotic tumor cells in tissue sections constitutes one of the strongest prognostic markers for breast cancer. David Tellez et al developed a method to automatically detect mitotic figures in H&E stained breast cancer tissue sections based on convolutional neural networks.
First edition MIDL 2018 great success
A big thank you to everyone who attended MIDL 2018 and made this first edition to a great success! Among 61 posters was work from Computational Pathology group members Hans Pinckaers, Zaneta Swiderska-Chadaj, David Tellez, Mart van Rijthoven and Wouter Bulten.
Radboud Science Award 2018 for Jeroen van der Laak and Geert Litjens
This week, the Radboud Science Award was awarded to Hanneke van Ouden, Thijs Eijsvogels, Jeroen van der Laak and Geert Litjens. In addition to recognizing excellent research, this award aims at connecting academic research to primary school teaching programs.