Deep orthogonal multi-wavelength fusion for tomogram-free diagnosis in diffuse optical imaging.

Autor: Ben Yedder H; Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, BC Canada V5A 1S6. Electronic address: hbenyedd@sfu.ca., Cardoen B; Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, BC Canada V5A 1S6., Shokoufi M; School of Mechatronic Systems Engineering, Simon Fraser University, BC Canada V5A 1S6., Golnaraghi F; School of Mechatronic Systems Engineering, Simon Fraser University, BC Canada V5A 1S6., Hamarneh G; Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, BC Canada V5A 1S6. Electronic address: hamarneh@sfu.ca.
Jazyk: angličtina
Zdroj: Computers in biology and medicine [Comput Biol Med] 2024 Aug; Vol. 178, pp. 108676. Date of Electronic Publication: 2024 May 28.
DOI: 10.1016/j.compbiomed.2024.108676
Abstrakt: Novel portable diffuse optical tomography (DOT) devices for breast cancer lesions hold great promise for non-invasive, non-ionizing breast cancer screening. Critical to this capability is not just the identification of lesions but rather the complex problem of discriminating between malignant and benign lesions. To accurately reconstruct the highly heterogeneous tissue of a cancer lesion in healthy breast tissue using DOT, multiple wavelengths can be leveraged to maximize signal penetration while minimizing sensitivity to noise. However, these wavelength responses can overlap, capture common information, and correlate, potentially confounding reconstruction and downstream end tasks. We show that an orthogonal fusion loss regularizes multi-wavelength DOT leading to improved reconstruction and accuracy of end-to-end discrimination of malignant versus benign lesions. We further show that our raw-to-task model significantly reduces computational complexity without sacrificing accuracy, making it ideal for real-time throughput, desired in medical settings where handheld devices have severely restricted power budgets. Furthermore, our results indicate that image reconstruction is not necessary for unbiased classification of lesions with a balanced accuracy of 77% and 66% on the synthetic dataset and clinical dataset, respectively, using the raw-to-task model. Code is available at https://github.com/sfu-mial/FuseNet.
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Hanene Ben Yedder reports financial support was provided by Natural Sciences and Engineering Research Council of Canada. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
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