The Innovative Medicine Initiative has awarded a 70 MEuro project to build the largest integrated database of digitized histopathologic slides and AI algorithms in the world.
The Fiets Pieten cycled a total of 355 km across the country, solving a special CPG mystery with help of their fellow crew members along the way.
Francesco Ciompi of the Computational Pathology group has received a prestigious NWO-TTW VIDI grant of 800,000 euro for his project "Predicting Lung Cancer Immunotherapy Response. It's personal".
In this project we aim to develop and validate AI techniques for the detection of serous tubal intra-epithelial carcinoma (STIC), a non-invasive lesion in the distal Fallopian tube which is expected to be a precursor for ovarian cancer.
Maschenka Balkenhol succesfully defended her PhD thesis titled 'Tissue-based biomarker assessment for predicting prognosis of triple negative breast cancer: the additional value of artificial intelligence' on the 15th of September.
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.