Digital pathology through machine learning analysis of breast cancer diagnostic slides.
Histopathological images are routinely produced for diagnosis and stratification of cancer. Image-based exercises are ideally suited for deep learning and are anticipated to provide significant time and cost-saving measures in precision oncology.
We hypothesize that machine learning algorithm can recognize important traits which could be helpful to guide diagnosis, treatment, or make a prognosis directly on the routinely performed H&E stain of breast tumors. In collaboration with Prof. Zohar Yakhini and Dr Øystein Garred, we have set up artificial neural network (a class of powerful machine learning methods), we train such machine learning algorithms to implement the field of digital pathology in breast cancer.