Cervical cancer remains a significant cause of mortality all around the world, even if it can be prevented and cured by removing affected tissues in early stages. Providing universal and efficient access to cervical screening programs is a challenge that requires identifying vulnerable individuals in the population, among other steps. In this work, we present a computationally automated strategy for predicting the outcome of the patient biopsy, given risk patterns from individual medical records. We propose a machine learning technique that allows a joint and fully supervised optimization of dimensionality reduction and classification models. We also build a model able to highlight relevant properties in the low dimensional space, to ease the classification of patients. We instantiated the proposed approach with deep learning architectures, and achieved accurate prediction results (top area under the curve AUC = 0.6875) which outperform previously developed methods, such as denoising autoencoders. Additionally, we explored some clinical findings from the embedding spaces, and we validated them through the medical literature, making them reliable for physicians and biomedical researchers.

Fernandes, K., Chicco, D., Cardoso, J., Fernandes, J. (2018). Supervised deep learning embeddings for the prediction of cervical cancer diagnosis. PEERJ. COMPUTER SCIENCE., 2018(5), 1-20 [10.7717/peerj-cs.154].

Supervised deep learning embeddings for the prediction of cervical cancer diagnosis

Chicco, D;
2018

Abstract

Cervical cancer remains a significant cause of mortality all around the world, even if it can be prevented and cured by removing affected tissues in early stages. Providing universal and efficient access to cervical screening programs is a challenge that requires identifying vulnerable individuals in the population, among other steps. In this work, we present a computationally automated strategy for predicting the outcome of the patient biopsy, given risk patterns from individual medical records. We propose a machine learning technique that allows a joint and fully supervised optimization of dimensionality reduction and classification models. We also build a model able to highlight relevant properties in the low dimensional space, to ease the classification of patients. We instantiated the proposed approach with deep learning architectures, and achieved accurate prediction results (top area under the curve AUC = 0.6875) which outperform previously developed methods, such as denoising autoencoders. Additionally, we explored some clinical findings from the embedding spaces, and we validated them through the medical literature, making them reliable for physicians and biomedical researchers.
Articolo in rivista - Articolo scientifico
Artificial neural networks; Autoencoder; Binary classification; Biomedical informatics; Cervical cancer; Deep learning; Denoising autoencoder; Dimensionality reduction; Health informatics; Health-care informatics
English
17-feb-2018
2018
2018
5
1
20
e154
open
Fernandes, K., Chicco, D., Cardoso, J., Fernandes, J. (2018). Supervised deep learning embeddings for the prediction of cervical cancer diagnosis. PEERJ. COMPUTER SCIENCE., 2018(5), 1-20 [10.7717/peerj-cs.154].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/430798
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