  Histopathology slides are routinely marked by pathologists using permanent
ink markers that should not be removed as they form part of the medical record.
Often tumour regions are marked up for the purpose of highlighting features or
other downstream processing such an gene sequencing. Once digitised there is no
established method for removing this information from the whole slide images
limiting its usability in research and study. Removal of marker ink from these
high-resolution whole slide images is non-trivial and complex problem as they
contaminate different regions and in an inconsistent manner. We propose an
efficient pipeline using convolution neural networks that results in ink-free
images without compromising information and image resolution. Our pipeline
includes a sequential classical convolution neural network for accurate
classification of contaminated image tiles, a fast region detector and a domain
adaptive cycle consistent adversarial generative model for restoration of
foreground pixels. Both quantitative and qualitative results on four different
whole slide images show that our approach yields visually coherent ink-free
whole slide images.
