The impact of scanner variations and stain normalization on CNN performance for prostate cancer classification on WSIs was investigated by Zaneta Swiderska-Chadaj and their colleagues, and the work was published in Nature Scientific Reports.
Due to memory constraints on current hardware, most convolutional neural networks (CNN) are trained on sub-megapixel images. A novel method for end-to-end training of CNNs on multi-megapixel images was proposed by Hans Pinckaers and his colleagues. Their work appeared online in IEEE Transactions on Pattern Analysis and Machine Intelligence.
After CAMELYON, there is now PANDA, our new challenge on prostate cancer grading in collaboration with Kaggle and Karolinska Institutet. Our aim is to crowdsource the best possible solution to help pathologists better diagnose and treat patients.
The Novartis Transplantation Award for Basic Research was awarded to the Computational Pathology group for the JASN publication on Deep learning for histopathologic renal tissue assessment by Meyke Hermsen et al.
The Gleason score suffers from significant inter-observer variability. This problem could be solved by the fully automated deep learning system developed by Wouter Bulten and his colleagues. Their work appeared online today in The Lancet Oncology.
The KNAW Early Career Award is intended to showcase talented PhD graduates, and to provide them with support and encouragement. The Award is aimed at researchers at the start of their career who are capable of developing innovative and original research ideas.
Jeroen van der Laak presented his keynote lecture entitled 'The rise of Artificial Intelligence and its impact on histopathology' at the 31rst European Computational Pathology Congress in the Acropolis Convention Centre in Nice, France.
Meyke Hermsen et al present the first multi-class segmentation network for the histopathological analysis of renal tissue. Their work was published online yesterday by the Journal of the American Society of Nephrology.
David Tellez et al present a new method to train neural networks on gigapixel whole-slide images directly, avoiding the need for fine-grained annotations. Their work appeared online yesterday in IEEE TPAMI.
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.