Dual-encoder deep learning networks to enhance diffuse optical imaging for highly scattered nonhomogeneous media

Nazish Murad; Ya-Fen Hsu; Min-Chun Pan

doi:10.1117/12.3008598

13 March 2024 Dual-encoder deep learning networks to enhance diffuse optical imaging for highly scattered nonhomogeneous media

Nazish Murad, Ya-Fen Hsu, Min-Chun Pan

Author Affiliations +

Proceedings Volume 12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences; 128570N (2024) https://doi.org/10.1117/12.3008598
Event: SPIE BiOS, 2024, San Francisco, California, United States

Conference Poster

Abstract

This paper aims to demonstrate a novel deep-learning network that addresses the prediction of breast tumors for diffuse optical imaging. Two learning schemes, signal encoder and image encoder, in the proposed network are designed for reconstructing optical-property images. The former processing method takes boundary data directly to deep networks, and predicts the optical-coefficient distribution, while the latter feeds images obtained by inverse image reconstruction with artifacts and sometimes hard-to-localized tumors. All 10,000 samples of synthesized homogeneous and heterogeneous phantoms were randomly selected for training, validation, and testing of performance. Twelve phantom samples were employed to justify its effectiveness in real applications.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Nazish Murad, Ya-Fen Hsu, and Min-Chun Pan "Dual-encoder deep learning networks to enhance diffuse optical imaging for highly scattered nonhomogeneous media", Proc. SPIE 12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences, 128570N (13 March 2024); https://doi.org/10.1117/12.3008598

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available