@inproceedings{4be2ab9b936b4b64a33eb8112ac9edd1,
title = "Combining symmetric and standard deep convolutional representations for detecting brain hemorrhage",
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.",
keywords = "brain hemorrhage, computer-aided detection, deep learning, deep symmetry-sensitive networks, machine learning, precision medicine, quantitative imaging",
author = "Arko Barman and Victor Lopez-Rivera and Songmi Lee and Vahidy, {Farhaan S.} and Fan, {James Z.} and Savitz, {Sean I.} and Sheth, {Sunil A.} and Luca Giancardo",
note = "Publisher Copyright: {\textcopyright} 2020 SPIE.; Medical Imaging 2020: Computer-Aided Diagnosis ; Conference date: 16-02-2020 Through 19-02-2020",
year = "2020",
doi = "10.1117/12.2549384",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Hahn, {Horst K.} and Mazurowski, {Maciej A.}",
booktitle = "Medical Imaging 2020",
address = "United States",
}