Semantic learning machine improves the CNN-based detection of prostate cancer in non-contrast-enhanced MRI

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Considering that Prostate Cancer (PCa) is the most frequently diagnosed tumor in Western men, considerable attention has been devoted in computer-assisted PCa detection approaches. However, this task still represents an open research question. In the clinical practice, multiparametric Magnetic Resonance Imaging (MRI) is becoming the most used modality, aiming at defining biomarkers for PCa. In the latest years, deep learning techniques have boosted the performance in prostate MR image analysis and classification. This work explores the use of the Semantic Learning Machine (SLM) neuroevolution algorithm to replace the backpropagation algorithm commonly used in the last fully-connected layers of Convolutional Neural Networks (CNNs). We analyzed the non-contrast-enhanced multispectral MRI sequences included in the PROSTATEx dataset, namely: T2-weighted, Proton Density weighted, Diffusion Weighted Imaging. The experimental results show that the SLM significantly outperforms XmasNet, a state-of-the-art CNN. In particular, with respect to XmasNet, the SLM achieves higher classification accuracy (without neither pre-training the underlying CNN nor relying on backprogation) as well as a speed-up of one order of magnitude.

Original languageEnglish
Title of host publicationGECCO 2019 Companion
Subtitle of host publicationProceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
EditorsManuel López-Ibáñez
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Pages1837-1845
Number of pages9
ISBN (Electronic)9781450367486
DOIs
Publication statusPublished - 13 Jul 2019
Event2019 Genetic and Evolutionary Computation Conference, GECCO 2019 - Prague, Czech Republic
Duration: 13 Jul 201917 Jul 2019

Conference

Conference2019 Genetic and Evolutionary Computation Conference, GECCO 2019
CountryCzech Republic
CityPrague
Period13/07/1917/07/19

Keywords

  • Convolutional Neural Networks
  • Neuroevolution
  • Non-contrast-enhanced MRI
  • Prostate cancer
  • Semantic Learning Machine

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