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

arXiv: https://arxiv.org/abs/2210.17360 · HTML: https://mlsanity.com/paper/2210.17360/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 50 references that are in the map.
- First posted on arXiv 31 Oct 2022.

## Builds on (most cited first, 19 of 19)

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- [Explaining NonLinear Classification Decisions with Deep Taylor Decomposition](https://mlsanity.com/paper/1512.02479/) (2015)
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- [Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks](https://mlsanity.com/paper/2107.11400/) (2021)
- [Deep learning in radiology: an overview of the concepts and a survey of the state of the art](https://mlsanity.com/paper/1802.08717/) (2018)
- [Deep transfer learning for image classification: a survey](https://mlsanity.com/paper/2205.09904/) (2022)
- [Distorted Representation Space Characterization Through Backpropagated Gradients](https://mlsanity.com/paper/1908.09998/) (2019)
- [HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach](https://mlsanity.com/paper/2006.07187/) (2020)
- [Direct Automatic Coronary Calcium Scoring in Cardiac and Chest CT](https://mlsanity.com/paper/1902.05408/) (2019)
- [An Iterative Scientific Machine Learning Approach for Discovery of Theories Underlying Physical Phenomena](https://mlsanity.com/paper/1909.13718/) (2019)

## Abstract (arXiv)

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.