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

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2018

Autores

Mafalda Falcão Ferreira (Autor) FEUP; Rui Camacho (Autor) FEUP; Luís Filipe Teixeira (Autor) FEUP

Ata de Conferência Internacional

Título: PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM)

Páginas: 629-632

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Madrid, SPAIN, DEC 03-06, 2018

Indexação

ISI Web of Knowledge - 6 Citações

Scopus - 7 Citações

INSPEC

Outras Informações

ID Authenticus: P-00Q-97A

DOI: 10.1109/BIBM.2018.8621356

Abstract (EN): Cancer is one of the most serious health problems of our time. One approach for automatically classifying tumor samples is to analyze derived molecular information. Previous work by Teixeira et al. compared different methods of Data Oversampling and Feature Reduction, as well as Deep (Stacked) Denoising Autoencoders followed by a shallow layer for classification. In this work, we compare the performance of 6 different types of Autoencoder (AE), combined with two different approaches when training the classification model: (a) fixing the weights, after pretraining an AE, and (b) allowing fine-tuning of the entire network. We also apply two different strategies for embedding the AE into the classification network: (1) by only importing the encoding layers, and (2) by importing the complete AE. Our best result was the combination of unsupervised feature learning through a single-layer Denoising AE, followed by its complete import into the classification network, and subsequent fine-tuning through supervised training, achieving an F1 score of 99.61% +/- 0.54. We conclude that a reconstruction of the input space, combined with a deeper classification network outperforms previous work, without resorting to data augmentation techniques.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 4