  Mycosis fungoides (MF) is a rare, potentially life threatening skin disease,
which in early stages clinically and histologically strongly resembles Eczema,
a very common and benign skin condition. In order to increase the survival
rate, one needs to provide the appropriate treatment early on. To this end, one
crucial step for specialists is the evaluation of histopathological slides
(glass slides), or Whole Slide Images (WSI), of the patients' skin tissue. We
introduce a deep learning aided diagnostics tool that brings a two-fold value
to the decision process of pathologists. First, our algorithm accurately
segments WSI into regions that are relevant for an accurate diagnosis,
achieving a Mean-IoU of 69% and a Matthews Correlation score of 83% on a novel
dataset. Additionally, we also show that our model is competitive with the
state of the art on a reference dataset. Second, using the segmentation map and
the original image, we are able to predict if a patient has MF or Eczema. We
created two models that can be applied in different stages of the diagnostic
pipeline, potentially eliminating life-threatening mistakes. The classification
outcome is considerably more interpretable than using only the WSI as the
input, since it is also based on the segmentation map. Our segmentation model,
which we call EU-Net, extends a classical U-Net with an EfficientNet-B7 encoder
which was pre-trained on the Imagenet dataset.
