Towards Highly Expressive Machine Learning Models of Non-Melanoma Skin Cancer. (arXiv:2207.05749v1 [cs.LG])

Pathologists have a rich vocabulary with which they can describe all the
nuances of cellular morphology. In their world, there is a natural pairing of
images and words. Recent advances demonstrate that machine learning models can
now be trained to learn high-quality image features and represent them as
discrete units of information. This enables natural language, which is also
discrete, to be jointly modelled alongside the imaging, resulting in a
description of the contents of the imaging. Here we present …

arxiv cancer learning lg machine machine learning machine learning models skin cancer