Deep Learning based detection of tumor tissue compartments improves prognostic immunoprofiling in muscle-invasive bladder cancer
Posted 11th November 2019 by Liv Sewell
Worldwide, bladder cancer (BC) is the 11th most commonly diagnosed cancer. In men, BC is the 7thmost commonly diagnosed cancer worldwide.Although men are more likely to develop BC than women, women present with more advanced disease and have worse survival rates.
Muscle-invasive bladder cancers (MIBC) are cancers that have grown into or through the muscle layers of the bladder wall.
Dr. Katharina Nekolla and Ansh Kapil and their team applied Deep Learning enabled pathology to better understand prognostic factors in MIBC. Here we review the significance of the research and share their original poster.
Digital Pathology and Deep Learning: AI assisting in PD-L1 scoring
Posted 25th September 2019 by Liv Sewell
Ahead of this year’s Digital Pathology and AI Congress in December we look back at some of the highlights from last year.
First up, Michel Vandenberghe’s presentation on a new deep learning algorithm, which demonstrates the potential of artificial Intelligence (AI) to support pathologists, has been developed for PD-L1 scoring in tumour cells and immune cells in urothelial carcinoma samples.