Autoencoders as Weight Initialization of Deep Classification Networks Applied to Papillary Thyroid Carcinoma

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 F 1 score of 98.04% ± 1.09 when identifying cancerous thyroid samples.

Using autoencoders as a weight initialization method on deep neural networks for disease detection

“…The rest of the experiments remained the same: 2 strategies for importing the pre-trained AE into the top layers of the classifier, two approaches when training the classifier to detect different types of cancer, same evaluation of the obtained results. Although in a different domain, the best outcome was reached with a combination of the same strategy and the same approach in the previous work [19], with an F 1 score of 98.04, when identifying thyroid cancer.…”

“…We extend the previously described work in [21] by assembling three different types of experiments, divided into two main parts, where we use three different AEs and five types of cancer samples. In the first one, we analyze the performance of a deep neural network (DNN), using the same pipeline to identify different types of cancer.…”

“…Additionally, we decided to gice priority to cancer types with high mortality and high incidence rates. We use the same thyroid, skin, and stomach datasets presented in [21], alongside the lung and breast datasets. The data filtering process in the cBioPortal comprised searching with the keywords PanCancer, sorting the obtained results from highest to lowest RNA-Seq examples, and finally selecting the thyroid, skin, stomach, breast, and lung datasets.…”

“…The authors in [19] developed a methodology for detecting papillary thyroid carcinoma. They analyzed how the usage of AEs as a weight initialization method affected the performance of a DNN.…”

“…In [21], Ferreira et al used the same methodology described in [19] to discriminate different types of cancer, instead of distinguishing cancerous samples from healthy ones. In this case, they aimed to identify thyroid, skin, and stomach cancer correctly.…”