System for Marking Tumor Regions in Gigapixel Histological Images


The main objective is the appraisal of the results of the investigation of new artificial intelligence algorithms for the automatic analysis of histological images (WSI) applied to the diagnosis of different types of cancers, among them (although not limited to): prostate cancer, triple negative breast cancer (TNBC), and skin cancer.



The latest technological advances have led to a drastic change in the possibilities of health care, thus improving the conditions of medical care. But today’s pathology services still rely heavily on the presence of qualified pathologists to recognize characteristic findings in a tissue section under a microscope.

Digital pathology, and innovation in this area, solves multiple problems related to both the development of work, the quality of service, as well as the patient (diagnosis and safety).

Therefore, the main objective of this project is to create a web platform for visualization, annotation, and automatic evaluation of histological cases that supports the identification of different types of cancer. This tool will allow pathologists from all over the world to obtain online diagnostic help based on artificial intelligence techniques.

As a main novelty, said system will host predictive models generated from the most innovative techniques in the field of deep learning. Using new digitized histological samples from any hospital in the world, the predictive models hosted in the cloud will be retrained with these cases using innovative active learning techniques.






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