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Mitochondrial diseases are currently untreatable due to our limitedunderstanding of their pathology. We study the expression of variousmitochondrial proteins in skeletal myofibres (SM) in order to discoverprocesses involved in mitochondrial pathology using Imaging Mass Cytometry(IMC). IMC produces high dimensional multichannel pseudo-images representingspatial variation in the expression of a panel of proteins within a tissue,including subcellular variation. Statistical analysis of these images requiressemi-automated annotation of thousands of SMs in IMC images of patient musclebiopsies. In this paper we investigate the use of deep learning (DL) on raw IMCdata to analyse it without any manual pre-processing steps, statisticalsummaries or statistical models. For this we first train state-of-art computervision DL models on all available image channels, both combined andindividually. We observed better than expected accuracy for many of thesemodels. We then apply state-of-the-art explainable techniques relevant tocomputer vision DL to find the basis of the predictions of these models. Someof the resulting visual explainable maps highlight features in the images thatappear consistent with the latest hypotheses about mitochondrial diseaseprogression within myofibres.

Explainable Deep Learning to Profile Mitochondrial Disease Using High Dimensional Protein Expression Data