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.
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.
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.
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.
The goal of this special issue is to attract and highlight the latest developments in computational pathology, and feature papers proposing state-of-the-art solutions in the field of digital pathology using advanced image analysis and artificial intelligence.