DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders

TL;DR

This paper introduces a deep convolutional autoencoder approach to generate invariant, compact representations of neural in situ hybridization images, significantly improving gene ontology classification accuracy over previous methods.

Contribution

The novel use of a deep convolutional denoising autoencoder for functional representation of complex neural images enhances classification performance in bio-imaging analysis.

Findings

Achieved an average AUC of 0.98, up from 0.92.

Reduced classification error by 75%.

Outperformed previous state-of-the-art methods.

Abstract

This paper presents a novel deep learning-based method for learning a functional representation of mammalian neural images. The method uses a deep convolutional denoising autoencoder (CDAE) for generating an invariant, compact representation of in situ hybridization (ISH) images. While most existing methods for bio-imaging analysis were not developed to handle images with highly complex anatomical structures, the results presented in this paper show that functional representation extracted by CDAE can help learn features of functional gene ontology categories for their classification in a highly accurate manner. Using this CDAE representation, our method outperforms the previous state-of-the-art classification rate, by improving the average AUC from 0.92 to 0.98, i.e., achieving 75% reduction in error. The method operates on input images that were downsampled significantly with respect to the original ones to make it computationally feasible.