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Title: A Hybrid End-to-End Approach Integrating Conditional Random Fields into CNNs for Prostate Cancer Detection on MRI
Authors: Lapa, Paulo
Castelli, Mauro
Gonçalves, Ivo 
Sala, Evis
Rundo, Leonardo
Keywords: prostate cancer detection; magnetic resonance imaging; convolutional neural networks; conditional random fields; recurrent neural networks
Issue Date: 2020
Publisher: MDPI
Project: UID/MULTI/00308/2019 
DSAIPA/DS/0022/2018 (GADgET) 
Slovenian Research Agency (research core funding No. P5-0410) 
The Mark Foundation for Cancer Research and Cancer Research UK Cambridge Centre [C9685/A25177] 
National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre 
Serial title, monograph or event: Applied Sciences (Switzerland)
Volume: 10
Issue: 1
Abstract: Prostate Cancer (PCa) is the most common oncological disease inWestern men. Even though a growing effort has been carried out by the scientific community in recent years, accurate and reliable automated PCa detection methods on multiparametric Magnetic Resonance Imaging (mpMRI) are still a compelling issue. In this work, a Deep Neural Network architecture is developed for the task of classifying clinically significant PCa on non-contrast-enhanced MR images. In particular, we propose the use of Conditional Random Fields as a Recurrent Neural Network (CRF-RNN) to enhance the classification performance of XmasNet, a Convolutional Neural Network (CNN) architecture specifically tailored to the PROSTATEx17 Challenge. The devised approach builds a hybrid end-to-end trainable network, CRF-XmasNet, composed of an initial CNN component performing feature extraction and a CRF-based probabilistic graphical model component for structured prediction, without the need for two separate training procedures. Experimental results show the suitability of this method in terms of classification accuracy and training time, even though the high-variability of the observed results must be reduced before transferring the resulting architecture to a clinical environment. Interestingly, the use of CRFs as a separate postprocessing method achieves significantly lower performance with respect to the proposed hybrid end-to-end approach. The proposed hybrid end-to-end CRF-RNN approach yields excellent peak performance for all the CNN architectures taken into account, but it shows a high-variability, thus requiring future investigation on the integration of CRFs into a CNN.
ISSN: 2076-3417
DOI: 10.3390/app10010338
Rights: openAccess
Appears in Collections:I&D INESCC - Artigos em Revistas Internacionais

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