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Proceedings Paper

Deep 3D convolutional neural network for automatic cancer tissue detection using multispectral photoacoustic imaging
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Paper Abstract

Multispectral photoacoustic (MPA) specimen imaging modality is proven successful in differentiating photoacoustic (PA) signal characteristics from a cancer and normal region. The oxy and de-oxy hemoglobin content in a human tissue captured in the MPA data are the key features for cancer detection. In this study, we propose to use deep 3D convolution neural network trained on the thyroid MPA dataset and tested on the prostate MPA dataset to evaluate this potential. The proposed algorithm first extracts the spatial, spectral, and temporal features from the thyroid MPA image data using 3D convolutional layers and detects cancer tissue using the logistic function, the last layer of the network. The model achieved an AUC (area under the curve) of the ROC (receiver operating characteristic) curve of 0.72 on the prostate MPA dataset.

Paper Details

Date Published: 15 March 2019
PDF: 8 pages
Proc. SPIE 10955, Medical Imaging 2019: Ultrasonic Imaging and Tomography, 109551D (15 March 2019); doi: 10.1117/12.2518686
Show Author Affiliations
Kamal Jnawali, Rochester Institute of Technology (United States)
Bhargava Chinni, Univ. of Rochester (United States)
Vikram Dogra, Univ. of Rochester (United States)
Saugata Sinha, Rochester Institute of Technology (United States)
Navalgund Rao, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 10955:
Medical Imaging 2019: Ultrasonic Imaging and Tomography
Brett C. Byram; Nicole V. Ruiter, Editor(s)

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