@inproceedings{bec8cd1f70074b41b5add883b8a12f77,
title = "USDL: Inexpensive medical imaging using Deep learning techniques and ultrasound technology",
abstract = "In this study, we present USDL, a novel model that employs deep learning algorithms in order to reconstruct and enhance corrupted ultrasound images. We utilize an unsupervised neural network called an autoencoder which works by compressing its input into a latent-space representation and then reconstructing the output from this representation. We trained our model on a dataset that compromises of 15,700 in vivo images of the neck, wrist, elbow, and knee vasculature and compared the quality of the images generated using the structural similarity index (SSIM) and peak to noise ratio (PSNR). In closely simulated conditions, the architecture exhibited an average reconstruction accuracy of 90% as indicated by our SSIM. Our study demonstrates that USDL outperforms state of the art image enhancement and reconstruction techniques in both image quality and computational complexity, while maintaining the architecture efficiency.",
keywords = "Autoencoders, Deep Learning, Denoising, In Vivo Ultrasounds, MSE, PSNR, SSIM, Speckle Noise, Ultrasound imaging",
author = "Manish Balamurugan and Kathryn Chung and Venkat Kuppoor and Smruti Mahapatra and Aliaksei Pustavoitau and Amir Manbachi",
note = "Publisher Copyright: Copyright {\textcopyright} 2020 ASME; 2020 Design of Medical Devices Conference, DMD 2020 ; Conference date: 06-04-2020 Through 09-04-2020",
year = "2020",
doi = "10.1115/DMD2020-9109",
language = "English (US)",
series = "Frontiers in Biomedical Devices, BIOMED - 2020 Design of Medical Devices Conference, DMD 2020",
publisher = "American Society of Mechanical Engineers (ASME)",
booktitle = "Frontiers in Biomedical Devices, BIOMED - 2020 Design of Medical Devices Conference, DMD 2020",
}