  Histological evaluation of tissue samples is a typical approach to identify
colorectal cancer metastases in the peritoneum. For immediate assessment,
reliable and real-time in-vivo imaging would be required. For example,
intraoperative confocal laser microscopy has been shown to be suitable for
distinguishing organs and also malignant and benign tissue. So far, the
analysis is done by human experts. We investigate the feasibility of automatic
colon cancer classification from confocal laser microscopy images using deep
learning models. We overcome very small dataset sizes through transfer learning
with state-of-the-art architectures. We achieve an accuracy of 89.1% for cancer
detection in the peritoneum which indicates viability as an intraoperative
decision support system.
