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

Nicolás Múnera Garzón; Charlems Alvarez-Jimenez; Fabio Gonzalez; Eduardo Romero

doi:10.1117/12.2542578

3 January 2020 Adaptive frequency saliency model based on convolutional neural networks: a case study for prostate cancer MRI

Nicolás Múnera Garzón, Charlems Alvarez-Jimenez, Fabio Gonzalez, Eduardo Romero

Author Affiliations +

Proceedings Volume 11330, 15th International Symposium on Medical Information Processing and Analysis; 113300B (2020) https://doi.org/10.1117/12.2542578
Event: 15th International Symposium on Medical Information Processing and Analysis, 2019, Medelin, Colombia

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.

Citation Download Citation

Nicolás Múnera Garzón, Charlems Alvarez-Jimenez, Fabio Gonzalez, and Eduardo Romero "Adaptive frequency saliency model based on convolutional neural networks: a case study for prostate cancer MRI", Proc. SPIE 11330, 15th International Symposium on Medical Information Processing and Analysis, 113300B (3 January 2020); https://doi.org/10.1117/12.2542578

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