Comparison of deep learned and texture features in mammographic mass classification

Guobin Li; Cory Thomas; Reyer Zwiggelaar

doi:10.1117/12.2625774

13 July 2022 Comparison of deep learned and texture features in mammographic mass classification

Guobin Li, Cory Thomas, Reyer Zwiggelaar

Proceedings Volume 12286, 16th International Workshop on Breast Imaging (IWBI2022); 122860N (2022) https://doi.org/10.1117/12.2625774
Event: Sixteenth International Workshop on Breast Imaging, 2022, Leuven, Belgium

Abstract

As deep learning models are increasingly applied in medical diagnostic assistance systems, this raises questions about ones ability to understand and interpret its decision-making process. In this work, using breast lesions from the Optimam Mammography Image Database (OMI-DB), we have explored whether deep learned features have similar predictive information as classical texture features. We trained a deep learning model for mass lesion classification and used Gradient-weighted Class Activation Mapping to produce a representation of deep learned features. Additional, classical texture features (e.g. energy) were extracted. Subsequently, we used the earth mover’s distance to investigate similarities between deep learned and texture features. The comparison identified that texture features such as mean, entropy and auto-correlation showed a strong similarity with the deep learned features and provided an indication of what the deep learning models might have used as information for its classification.

Citation Download Citation

Guobin Li, Cory Thomas, and Reyer Zwiggelaar "Comparison of deep learned and texture features in mammographic mass classification", Proc. SPIE 12286, 16th International Workshop on Breast Imaging (IWBI2022), 122860N (13 July 2022); https://doi.org/10.1117/12.2625774

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KEYWORDS

Feature extraction

Image classification

Visualization

Breast

Image filtering

Breast cancer

Mammography

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