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.
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.
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.
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.
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.
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.
Peter Bandi and Oscar Geessink challenged participants to move from individual metastases detection to classification of lymph node status on a patient level. The algorithmic details of the twelve best submissions are discussed in the paper that appeared in IEEE's TMI last August.
Jeroen van der Laak has contributed to a news article from NOS about the success of Google's deep learning-based algorithm for the automatic detection of breast cancer metastasis in sentinel lymphnodes. The algorithm was trained using data from the grand challenge CAMELYON16, organized by members of the CP group. The …
CAMELYON16 was the first medical image analysis challenge with whole slide digital pathology images in 2016. The competition was a great success, and several of the submitted software solutions outperformed human pathologists in the detection of lymph node metastases.