Paper Info
Title
Semantic learning machine improves the CNN-Based detection of prostate cancer in non-contrast-enhanced MRI.
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.
Year
DOI
Venue
2019
10.1145/3319619.3326864
GECCO
Keywords
Field
DocType
Prostate cancer, Non-contrast-enhanced MRI, Semantic Learning Machine, Neuroevolution, Convolutional Neural Networks
Diffusion MRI,Multiparametric Magnetic Resonance Imaging,Computer science,Convolutional neural network,Multispectral image,Artificial intelligence,Prostate cancer,Deep learning,Neuroevolution,Backpropagation,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4503-6748-6
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Paulo Lapa100.68
Ivo Gonçalves2638.97
Leonardo Rundo3256.40
Mauro Castelli459556.31