  Mitochondrial diseases are currently untreatable due to our limited
understanding of their pathology. We study the expression of various
mitochondrial proteins in skeletal myofibres (SM) in order to discover
processes involved in mitochondrial pathology using Imaging Mass Cytometry
(IMC). IMC produces high dimensional multichannel pseudo-images representing
spatial variation in the expression of a panel of proteins within a tissue,
including subcellular variation. Statistical analysis of these images requires
semi-automated annotation of thousands of SMs in IMC images of patient muscle
biopsies. In this paper we investigate the use of deep learning (DL) on raw IMC
data to analyse it without any manual pre-processing steps, statistical
summaries or statistical models. For this we first train state-of-art computer
vision DL models on all available image channels, both combined and
individually. We observed better than expected accuracy for many of these
models. We then apply state-of-the-art explainable techniques relevant to
computer vision DL to find the basis of the predictions of these models. Some
of the resulting visual explainable maps highlight features in the images that
appear consistent with the latest hypotheses about mitochondrial disease
progression within myofibres.
