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Cancer is still one of the most devastating diseases of our time. One way ofautomatically classifying tumor samples is by analyzing its derived molecularinformation (i.e., its genes expression signatures). In this work, we aim todistinguish three different types of cancer: thyroid, skin, and stomach. Forthat, we compare the performance of a Denoising Autoencoder (DAE) used asweight initialization of a deep neural network. Although we address a differentdomain problem in this work, we have adopted the same methodology of Ferreiraet al.. In our experiments, we assess two different approaches when trainingthe 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 theclassification network: (1) by only importing the encoding layers, and (2) byinserting the complete autoencoder. Our best result was the combination ofunsupervised feature learning through a DAE, followed by its full import intothe classification network, and subsequent fine-tuning through supervisedtraining, achieving an F1 score of 98.04% +/- 1.09 when identifying cancerousthyroid samples.

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