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Adaptive frequency saliency model based on convolutional neural networks: a case study for prostate cancer MRI
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Paper Abstract

This article introduces a novel adaptive frequency saliency model (AFSM) that selects relevant information by filtering an image with a set of band pass filters optimally placed in the frequency space using an auto- encoder CNN. The obtained images show a higher signal-to-noise ratio and therefore they improve a classifier performance. The proposed method is challenged by a classification task: prostate magnetic resonance imaging (MRI) to be labeled as cancerous or non-cancerous tissue. Evaluation in this case was carried out by training a convolutional neural network (CNN) with a prostate dataset but at the testing phase, the trained model is assessed with non-filtered and filtered images. The classifier tried with filtered images outperformed the results obtained with the non filtered ones (classification accuracy scores of 0.792± 0.016 and 0.776± 0.036 respectively), demonstrating better overall performance and the importance of using filtering processes.

Paper Details

Date Published: 3 January 2020
PDF: 6 pages
Proc. SPIE 11330, 15th International Symposium on Medical Information Processing and Analysis, 113300B (3 January 2020); doi: 10.1117/12.2542578
Show Author Affiliations
Nicolás Múnera Garzón, Univ. Nacional de Colombia (Colombia)
Charlems Alvarez-Jimenez, Univ. Nacional de Colombia (Colombia)
Fabio Gonzalez, Univ. Nacional de Colombia (Colombia)
Eduardo Romero, Univ. Nacional de Colombia (Colombia)


Published in SPIE Proceedings Vol. 11330:
15th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Jorge Brieva, Editor(s)

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