Autoencoders as Weight Initialization of Deep Classification Networks for Cancer versus Cancer Studies

TL;DR

This study explores using denoising autoencoders for weight initialization in deep neural networks to improve cancer type classification from gene expression data, achieving high accuracy especially for thyroid cancer.

Contribution

It introduces a methodology combining unsupervised autoencoder pre-training with supervised fine-tuning for cancer classification, comparing different embedding and training strategies.

Findings

Best F1 score of 98.04% for thyroid cancer classification.

Full autoencoder import with fine-tuning yields superior performance.

Autoencoder-based initialization enhances deep network accuracy in cancer diagnosis.

Abstract

Cancer is still one of the most devastating diseases of our time. One way of automatically classifying tumor samples is by analyzing its derived molecular information (i.e., its genes expression signatures). In this work, we aim to distinguish three different types of cancer: thyroid, skin, and stomach. For that, we compare the performance of a Denoising Autoencoder (DAE) used as weight initialization of a deep neural network. Although we address a different domain problem in this work, we have adopted the same methodology of Ferreira et al.. In our experiments, we assess two different approaches when training the classification model: (a) fixing the weights, after pre-training the DAE, and (b) allowing fine-tuning of the entire classification network. Additionally, we apply two different strategies for embedding the DAE into the classification network: (1) by only importing the encoding layers, and (2) by inserting the complete autoencoder. Our best result was the combination of unsupervised feature learning through a DAE, followed by its full import into the classification network, and subsequent fine-tuning through supervised training, achieving an F1 score of 98.04% +/- 1.09 when identifying cancerous thyroid samples.