@inproceedings{c8a8bff08e894c8bba918f01da1151a6,
title = "BNCNN based diffuse optical imaging",
abstract = "We proposed and implemented a deep learning scheme using convolution neural networks (CNNs) with batch normalization (BNCNN) to construct a sensor-image DOI computation model with the aim of reconstructing tissue optical-property images as well as identifying and localizing breast tumors. A non-iterative learning reconstruction method was developed to recover optical properties, focusing on one-dimensional convolution layers followed by dense layers. Besides simulated data for model training, validation and testing, for the comparison of model performance, measurement data sets were employed to test on the same trained network which results outperform Tikhonov regularization method and other artificial neural networks as well.",
keywords = "Batch normalization, Convolution deep neural networks, Sensor to image domain, diffuse optical imaging",
author = "Nazish Murad and Pan, {Min Chun} and Hsu, {Ya Fen}",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; Multimodal Biomedical Imaging XVII 2022 ; Conference date: 22-01-2022 Through 27-01-2022",
year = "2022",
doi = "10.1117/12.2609193",
language = "???core.languages.en_GB???",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Azar, {Fred S.} and Xavier Intes and Qianqian Fang",
booktitle = "Multimodal Biomedical Imaging XVII",
}