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Tomography
Volume 5
Issue 1
10.18383/j.tom.2018.00034
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Tomography is published by MDPI from Volume 7 Issue 1 (2021). Previous articles were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence, and they are hosted by MDPI on mdpi.com as a courtesy and upon agreement with Grapho, LLC.
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Article

Explaining Deep Features Using Radiologist-Defined Semantic Features and Traditional Quantitative Features

by
Rahul Paul
1,
Matthew Schabath
2,
Yoganand Balagurunathan
3,
Ying Liu
4,
Qian Li
4,
Robert Gillies
3,
Lawrence O. Hall
1 and
Dmitry B. Goldgof
1,*
1
Department of Computer Science & Engineering, USF College of Engineering, Building II 4220 E. Fowler Avenue, Tampa, FL 33620, USA
2
Department of Cancer Epidemiology, H. L. Moffitt Cancer Center & Research Institute, Tampa, FL 33620, USA
3
Department of Cancer Imaging and Metabolism, H. L. Moffitt Cancer Center & Research Institute, Tampa, FL 33620, USA
4
Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin 300051, China
*
Author to whom correspondence should be addressed.
Tomography 2019, 5(1), 192-200; https://doi.org/10.18383/j.tom.2018.00034
Submission received: 24 January 2019 / Revised: 6 February 2019 / Accepted: 23 February 2019 / Published: 1 March 2019
Versions Notes

Abstract

Quantitative features are generated from a tumor phenotype by various data characterization, feature-extraction approaches and have been used successfully as a biomarker. These features give us information about a nodule, for example, nodule size, pixel intensity, histogram-based information, and texture information from wavelets or a convolution kernel. Semantic features, on the other hand, can be generated by an experienced radiologist and consist of the common characteristics of a tumor, for example, location of a tumor, fissure, or pleural wall attachment, presence of fibrosis or emphysema, concave cut on nodule surface. These features have been derived for lung nodules by our group. Semantic features have also shown promise in predicting malignancy. Deep features from images are generally extracted from the last layers before the classification layer of a convolutional neural network (CNN). By training with the use of different types of images, the CNN learns to recognize various patterns and textures. But when we extract deep features, there is no specific naming approach for them, other than denoting them by the feature column number (position of a neuron in a hidden layer). In this study, we tried to relate and explain deep features with respect to traditional quantitative features and semantic features. We discovered that 26 deep features from the Vgg-S neural network and 12 deep features from our trained CNN could be explained by semantic or traditional quantitative features. From this, we concluded that those deep features can have a recognizable definition via semantic or quantitative features.
Keywords: deep features; radiomics; semantic features; interpretation of features; CNN; explainable AI; quantitative features deep features; radiomics; semantic features; interpretation of features; CNN; explainable AI; quantitative features

Share and Cite

MDPI and ACS Style

Paul, R.; Schabath, M.; Balagurunathan, Y.; Liu, Y.; Li, Q.; Gillies, R.; Hall, L.O.; Goldgof, D.B. Explaining Deep Features Using Radiologist-Defined Semantic Features and Traditional Quantitative Features. Tomography 2019, 5, 192-200. https://doi.org/10.18383/j.tom.2018.00034

AMA Style

Paul R, Schabath M, Balagurunathan Y, Liu Y, Li Q, Gillies R, Hall LO, Goldgof DB. Explaining Deep Features Using Radiologist-Defined Semantic Features and Traditional Quantitative Features. Tomography. 2019; 5(1):192-200. https://doi.org/10.18383/j.tom.2018.00034

Chicago/Turabian Style

Paul, Rahul, Matthew Schabath, Yoganand Balagurunathan, Ying Liu, Qian Li, Robert Gillies, Lawrence O. Hall, and Dmitry B. Goldgof. 2019. "Explaining Deep Features Using Radiologist-Defined Semantic Features and Traditional Quantitative Features" Tomography 5, no. 1: 192-200. https://doi.org/10.18383/j.tom.2018.00034

APA Style

Paul, R., Schabath, M., Balagurunathan, Y., Liu, Y., Li, Q., Gillies, R., Hall, L. O., & Goldgof, D. B. (2019). Explaining Deep Features Using Radiologist-Defined Semantic Features and Traditional Quantitative Features. Tomography, 5(1), 192-200. https://doi.org/10.18383/j.tom.2018.00034

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