Source: medicalxpress.com Author: University of Warwick Whole slide images are multi-gigapixel images and cannot be used directly for image analysis tasks particularly training a deep learning based classifier. Therefore, we divide the WSIs into small regions (patches) for processing. A deep learning based classifier is applied on the patches to identify whether the patch contains tumour, lymphocytes or other histological primitives. However, the regions where the lymphocytes are infiltrating the tumour may not be confined within a patch. Besides, there is considerable variation in the size of TIL regions, making the quantification of TILs a non-trivial task. We address this issue by adopting the widely accepted definition of TILs, i.e., lymphocytes that lie in the neighbourhood of tumour areas. The patch labels predicted as lymphocytes or tumour are then used to compute a statistical measure of co-localization, which is further incorporated into the computation of the TILAb score of lymphocytic infiltration. Credit: University of Warwick   The chances of surviving oral cancers can be predicted by state of the art AI algorithms—developed by scientists at the Department of Computer Science at the University of Warwick—that precisely calculate the abundance of immune cells in the midst of tumour cells to help better understand the spread of and resistance to cancer. In 2014 there were more than 11,000 cases of head and neck cancers in the UK and more than 2,300 deaths resulting from the most common of them; oral cavity cancer. Oral cancer is most prevalent in South Asia, particularly India, Pakistan [...]