Share Email Print

Proceedings Paper

Siamese neural networks for the classification of high-dimensional radiomic features
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

This study demonstrates that a variant of a Siamese neural network architecture is more effective at classifying highdimensional radiomic features (extracted from T2 MRI images) than traditional models, such as a Support Vector Machine or Discriminant Analysis. Ninety-nine female patients, between the ages of 20 and 48, were imaged with T2 MRI. Using biopsy pathology, the patients were separated into two groups: those with breast cancer (N=55) and those with GLM (N=44). Lesions were segmented by a trained radiologist and the ROIs were used for radiomic feature extraction. The radiomic features include 536 published features from Aerts et al., along with 20 features recurrent quantification analysis features. A Student T-Test was used to select features found to be statistically significant between the two patient groups. These features were then used to train a Siamese neural network. The label given to test features was the label of whichever class the test features with the highest percentile similarity within the training group. Within the two highest-dimensional feature sets, the Siamese network produced an AUC of 0.853 and 0.894, respectively. This is compared to best non-Siamese model, Discriminant Analysis, which produced an AUC of 0.823 and 0.836 for the two respective feature sets. However, when it came to the lower-dimensional recurrent features and the top-20 most significant features from Aerts et al., the Siamese network performed on-par or worse than the competing models. The proposed Siamese neural network architecture can outperform competing other models in high-dimensional, low-sample size spaces with regards to tabular data.

Paper Details

Date Published: 16 March 2020
PDF: 9 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113143Q (16 March 2020); doi: 10.1117/12.2549389
Show Author Affiliations
Abhishaike Mahajan, The Univ. of Texas at Dallas (United States)
James Dormer, The Univ. of Texas at Dallas (United States)
Qinmei Li, The Univ. of Texas at Dallas (United States)
The Second Affiliated Hospital of Guangzhou Medical Univ. (China)
Deji Chen, The Second Affiliated Hospital of Guangzhou Medical Univ. (China)
Zhenfeng Zhang, The Second Affiliated Hospital of Guangzhou Medical Univ. (China)
Baowei Fei, The Univ. of Texas at Dallas (United States)

Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?