Detalhes
Nome
Mafalda Falcão FerreiraCluster
InformáticaCargo
Assistente de InvestigaçãoDesde
01 maio 2018
Nacionalidade
PortugalCentro
Laboratório de Inteligência Artificial e Apoio à DecisãoContactos
+351220402963
mafalda.f.torres@inesctec.pt
2020
Autores
Ferreira, MF; Savoy, JN; Markey, MK;
Publicação
BREAST
Abstract
2020
Autores
Ferreira, MF;
Publicação
PROCEEDINGS OF THE 35TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING (SAC'20)
Abstract
2020
Autores
Ferreira, MF; Camacho, R; Teixeira, LF;
Publicação
BMC MEDICAL INFORMATICS AND DECISION MAKING
Abstract
2018
Autores
Ferreira, MF; Camacho, R; Teixeira, LF;
Publicação
PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM)
Abstract
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.
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