The State of Applying Artificial Intelligence to Tissue Imaging for
  Cancer Research and Early Detection
Michael Robben Amir Hajighasemi Mohammad Sadegh Nasr Jai Prakesh Veerla Anne M. Alsup Biraaj Rout Helen H. Shang Kelli Fowlds Parisa Boodaghi Malidarreh Paul Koomey MD Jillur Rahman Saurav Jacob M. Luber
Artificial intelligence represents a new frontier in human medicine that
could save more lives and reduce the costs, thereby increasing accessibility.
As a consequence, the rate of advancement of AI in cancer medical imaging and
more particularly tissue pathology has exploded, opening it to ethical and
technical questions that could impede its adoption into existing systems. In
order to chart the path of AI in its application to cancer tissue imaging, we
review current work and identify how it can improve cancer pathology
diagnostics and research. In this review, we identify 5 core tasks that models
are developed for, including regression, classification, segmentation,
generation, and compression tasks. We address the benefits and challenges that
such methods face, and how they can be adapted for use in cancer prevention and
treatment. The studies looked at in this paper represent the beginning of this
field and future experiments will build on the foundations that we highlight.