Anomaly detection inside diffuse media using deep learning algorithm

Ben Wiesel; Shlomi Arnon

Anomaly detection inside diffuse media using deep learning algorithm

Ben Wiesel
, Shlomi Arnon

Department of Electrical & Computer Engineering

Research output: Contribution to journal › Conference article › peer-review

Abstract

Novel deep-learning algorithm is utilized to detect simulated tumors embedded inside 3D turbid media using a small set of sources and detectors. Thus, proving the utility of deep-learning methods for solving diffuse imaging 3D inverse problems.

Original language	English
Article number	ETu2A.8
Journal	Optics InfoBase Conference Papers
State	Published - 1 Jan 2021
Event	2021 European Conferences on Biomedical Optics, ECBO 2021 - Virtual, Online, Germany Duration: 20 Jun 2021 → 24 Jun 2021

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Mechanics of Materials

Cite this

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title = "Anomaly detection inside diffuse media using deep learning algorithm",

abstract = "Novel deep-learning algorithm is utilized to detect simulated tumors embedded inside 3D turbid media using a small set of sources and detectors. Thus, proving the utility of deep-learning methods for solving diffuse imaging 3D inverse problems.",

author = "Ben Wiesel and Shlomi Arnon",

note = "Publisher Copyright: {\textcopyright}2021TheAuthor(s); 2021 European Conferences on Biomedical Optics, ECBO 2021 ; Conference date: 20-06-2021 Through 24-06-2021",

year = "2021",

month = jan,

day = "1",

language = "English",

journal = "Optics InfoBase Conference Papers",

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TY - JOUR

T1 - Anomaly detection inside diffuse media using deep learning algorithm

AU - Wiesel, Ben

AU - Arnon, Shlomi

PY - 2021/1/1

Y1 - 2021/1/1

N2 - Novel deep-learning algorithm is utilized to detect simulated tumors embedded inside 3D turbid media using a small set of sources and detectors. Thus, proving the utility of deep-learning methods for solving diffuse imaging 3D inverse problems.

AB - Novel deep-learning algorithm is utilized to detect simulated tumors embedded inside 3D turbid media using a small set of sources and detectors. Thus, proving the utility of deep-learning methods for solving diffuse imaging 3D inverse problems.

UR - https://www.scopus.com/pages/publications/85119455931

M3 - Conference article

AN - SCOPUS:85119455931

SN - 2162-2701

JO - Optics InfoBase Conference Papers

JF - Optics InfoBase Conference Papers

M1 - ETu2A.8

T2 - 2021 European Conferences on Biomedical Optics, ECBO 2021

Y2 - 20 June 2021 through 24 June 2021

ER -

Anomaly detection inside diffuse media using deep learning algorithm

Abstract

ASJC Scopus subject areas

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