Utilizing Autoencoders for Analysis and Classification of Microscopic in Situ Hybridization Images

Abstract

Currently, analysis of microscopic In Situ Hybridization (ISH) images is done manually by experts. Precise evaluation and classification of such microscopic images can ease experts' work and reveal further insights about the data. In this work, we propose a deep-learning workflow to detect and classify areas of microscopic images with similar levels of gene expression. Analysis of the data is done by employing a type of ANN – Deep Learning Autoencoders – suitable for unsupervised learning. The model's performance is optimised by balancing the latent layers' length and complexity and fine-tuning hyperparameters. The results are validated by adapting the mean-squared error (MSE) metric and comparison to expert's evaluation. Reconstruction of the whole-scale microscopic images is used to summarise and visualise the results.