TY - GEN
T1 - Enhancing classification performance of convolutional neural networks for prostate cancer detection on magnetic resonance images
T2 - 2019 Genetic and Evolutionary Computation Conference, GECCO 2019
AU - Lapa, Paulo
AU - Rundo, Leonardo
AU - Gonçalves, Ivo
AU - Castelli, Mauro
N1 - info:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0022%2F2018/PT#
Lapa, P., Rundo, L., Gonçalves, I., & Castelli, M. (2019). Enhancing classification performance of convolutional neural networks for prostate cancer detection on magnetic resonance images: A study with the semantic learning machine. In GECCO 2019 : Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion (pp. 381-382). (GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion). Association for Computing Machinery, Inc. https://doi.org/10.1145/3319619.3322035 --- This work was partially supported by projects UID/MULTI/00308/2019 and by the European Regional Development Fund through the COMPETE 2020 Programme, FCT - Portuguese Foundation for Science and Technology and Regional Operational Program of the Center Region (CENTRO2020) within project MAnAGER (POCI-01-0145-FEDER-028040). This work was also partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under project DSAIPA/DS/0022/2018 (GADgET).
PY - 2019/7/13
Y1 - 2019/7/13
N2 - Prostate cancer (PCa) is the most common oncological disease in Western men. Even though a significant effort has been carried out by the scientific community, accurate and reliable automated PCa detection methods are still a compelling issue. In this clinical scenario, high-resolution multiparametric Magnetic Resonance Imaging (MRI) is becoming the most used modality, also enabling quantitative studies. Recently, deep learning techniques have achieved outstanding results in prostate MRI analysis tasks, in particular with regard to image classification. This paper studies the feasibility of using the Semantic Learning Machine (SLM) neuroevolution algorithm to replace the fully-connected architecture commonly used in the last layers of Convolutional Neural Networks (CNNs). The experimental phase considered the PROSTATEx dataset composed of multispectral MRI sequences. The achieved results show that, on the same non-contrast-enhanced MRI series, SLM outperforms with statistical significance a state-of-the-art CNN trained with backpropagation. The SLM performance is achieved without pre-training the underlying CNN with backpropagation. Furthermore, on average the SLM training time is approximately 14 times faster than the backpropagation-based approach.
AB - Prostate cancer (PCa) is the most common oncological disease in Western men. Even though a significant effort has been carried out by the scientific community, accurate and reliable automated PCa detection methods are still a compelling issue. In this clinical scenario, high-resolution multiparametric Magnetic Resonance Imaging (MRI) is becoming the most used modality, also enabling quantitative studies. Recently, deep learning techniques have achieved outstanding results in prostate MRI analysis tasks, in particular with regard to image classification. This paper studies the feasibility of using the Semantic Learning Machine (SLM) neuroevolution algorithm to replace the fully-connected architecture commonly used in the last layers of Convolutional Neural Networks (CNNs). The experimental phase considered the PROSTATEx dataset composed of multispectral MRI sequences. The achieved results show that, on the same non-contrast-enhanced MRI series, SLM outperforms with statistical significance a state-of-the-art CNN trained with backpropagation. The SLM performance is achieved without pre-training the underlying CNN with backpropagation. Furthermore, on average the SLM training time is approximately 14 times faster than the backpropagation-based approach.
KW - Classification
KW - Convolutional Neural Networks
KW - Multiparamet-ric Magnetic Resonance Imaging
KW - Neuroevolution
KW - Prostate cancer detection
KW - Semantic Learning Machine
UR - http://www.scopus.com/inward/record.url?scp=85066949538&partnerID=8YFLogxK
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000538328100190
U2 - 10.1145/3319619.3322035
DO - 10.1145/3319619.3322035
M3 - Conference contribution
T3 - GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
SP - 381
EP - 382
BT - GECCO 2019
PB - ACM - Association for Computing Machinery
Y2 - 13 July 2019 through 17 July 2019
ER -