  Frozen sectioning (FS) is the preparation method of choice for microscopic
evaluation of tissues during surgical operations. The high speed of the
procedure allows pathologists to rapidly assess the key microscopic features,
such as tumour margins and malignant status to guide surgical decision-making
and minimise disruptions to the course of the operation. However, FS is prone
to introducing many misleading artificial structures (histological artefacts),
such as nuclear ice crystals, compression, and cutting artefacts, hindering
timely and accurate diagnostic judgement of the pathologist. Additional
training and prolonged experience is often required to make highly effective
and time-critical diagnosis on frozen sections. On the other hand, the gold
standard tissue preparation technique of formalin-fixation and
paraffin-embedding (FFPE) provides significantly superior image quality, but is
a very time-consuming process (12-48 hours), making it unsuitable for
intra-operative use. In this paper, we propose an artificial intelligence (AI)
method that improves FS image quality by computationally transforming
frozen-sectioned whole-slide images (FS-WSIs) into whole-slide FFPE-style
images in minutes. AI-FFPE rectifies FS artefacts with the guidance of an
attention mechanism that puts a particular emphasis on artefacts while
utilising a self-regularization mechanism established between FS input image
and synthesized FFPE-style image that preserves clinically relevant features.
As a result, AI-FFPE method successfully generates FFPE-style images without
significantly extending tissue processing time and consequently improves
diagnostic accuracy. We demonstrate the efficacy of AI-FFPE on lung and brain
frozen sections using a variety of different qualitative and quantitative
metrics including visual Turing tests from 20 board certified pathologists.
