Highlights

Results CAMELYON17 challenge

Results CAMELYON17 challenge

Peter Bandi and Oscar Geessink, organizers of CAMELYON17, challenged participants to move from individual metastases detection (CAMELYON16) to classification of lymph node status on a patient level. Over 300 participants registered on the challenge website, of which 23 teams submitted a total of 37 algorithms before the deadline. The algorithmic … Read more →

Radboud Science Award 2018 for Jeroen van der Laak and Geert Litjens

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. Prior to and during the award ceremony, the winners were asked … Read more →

Automatic mitosis detection in breast cancer tissue sections

Automatic mitosis detection in breast cancer tissue sections

Manual counting of mitotic tumor cells in tissue sections constitutes one of the strongest prognostic markers for breast cancer. This procedure, however, is time-consuming and error-prone. David Tellez et al developed a method to automatically detect mitotic figures in H&E stained breast cancer tissue sections based on convolutional neural … Read more →

First edition MIDL 2018 great success

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. Read more →

CAMELYON16 published in JAMA

CAMELYON16 published in JAMA

The Diagnostic Image Analysis Group organized CAMELYON16, 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. The results of Camelyon16 were published … Read more →

NOS news article

NOS news article

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 … Read more →