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Proceedings Paper

Combining symmetric and standard deep convolutional representations for detecting brain hemorrhage
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Paper Abstract

Brain hemorrhage (BH) is a severe type of stroke resulting in high mortality and morbidity. Detection and diagnosis of BH is commonly performed using neuroimaging tools such as Computed Tomography (CT). We compare and contrast symmetry-aware, symmetry-naive feature representations and their combination for the detection of BH using CT imaging. One of the proposed architectures, e-DeepSymNet, achieves AUC 0.99 [0.97- 1.00] for BH detection. An analysis of the activation values shows that both symmetry-aware and symmetry-naive representations offer complementary information with symmetry-aware representation naive contributing 20% towards the final predictions.

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113140D (16 March 2020); doi: 10.1117/12.2549384
Show Author Affiliations
Arko Barman, The Univ. of Texas Health Science Ctr. at Houston (United States)
Victor Lopez-Rivera, The Univ. of Texas Health Science Ctr. at Houston (United States)
Songmi Lee, The Univ. of Texas Health Science Ctr. at Houston (United States)
Farhaan S. Vahidy, The Univ. of Texas Health Science Ctr. at Houston (United States)
James Z. Fan, The Univ. of Texas Health Science Ctr. at Houston (United States)
Sean I. Savitz, The Univ. of Texas Health Science Ctr. at Houston (United States)
Sunil A. Sheth, The Univ. of Texas Health Science Ctr. at Houston (United States)
Luca Giancardo, The Univ. of Texas Health Science Ctr. at Houston (United States)


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

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