東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Integrating Coherent Anti-Stokes Raman Scattering Imaging and Deep Learning Analytics for High Precision, Real Time, Label Free Cancer Diagnosis.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Integrating Coherent Anti-Stokes Raman Scattering Imaging and Deep Learning Analytics for High Precision, Real Time, Label Free Cancer Diagnosis./
作者:	Weng, Sheng.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	166 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-04, Section: B.
Contained By:	Dissertations Abstracts International83-04B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28736222
ISBN:	9798535526096

Integrating Coherent Anti-Stokes Raman Scattering Imaging and Deep Learning Analytics for High Precision, Real Time, Label Free Cancer Diagnosis.
Weng, Sheng.

Integrating Coherent Anti-Stokes Raman Scattering Imaging and Deep Learning Analytics for High Precision, Real Time, Label Free Cancer Diagnosis. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 166 p.

Source: Dissertations Abstracts International, Volume: 83-04, Section: B.

Thesis (Ph.D.)--Rice University, 2017.

This item must not be sold to any third party vendors.

Coherent anti-Stokes Raman scattering (CARS) imaging technique has demonstrated great potential in clinical diagnosis by providing cellular-level resolution images without using exogenous contrast agents. This thesis contributes to the formation of an optical fiber based signal collection scheme and an automated image analytics platform to translate CARS microscopy for clinical uses. First, I introduce the concept of CARS by showing original images acquired from thyroid and parathyroid tissues. Second, I describe the use of a customized optical fiber bundle to collect and differentiate forward and backward generated CARS signals that contain different structural information. Third, I demonstrate the feasibility of using deep learning algorithms to characterize and classify CARS images automatically. In particular, I apply transfer learning on the CARS images and achieve 89.2% prediction accuracy in differentiating normal, small-cell carcinoma, adenocarcinoma, and squamous cell carcinoma human lung images. The combination of an optical fiber based microendoscopy and deep learning image classification algorithm will facilitate CARS imaging for on-the-spot cancer diagnosis, allowing medical practitioners to obtain essential information in real time and accelerate clinical decision-making. Meanwhile, the thesis also shows the generality of the deep learning algorithm developed by classifying screening images generated in drug discovery. As an example, for automated classification of large volumes of high-content screening images for Alzheimer's disease drug discovery, by applying similar transfer learning method on hyperphosphorylated tau images, I categorize drug hits into ineffective, partially-effective, and significantly-effective groups with high speed and accuracy.

ISBN: 9798535526096Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Nonlinear imaging

Integrating Coherent Anti-Stokes Raman Scattering Imaging and Deep Learning Analytics for High Precision, Real Time, Label Free Cancer Diagnosis.
LDR:03276nmm a2200469 4500 001 2348656
005 20220912135628.5
008 241004s2017 ||||||||||||||||| ||eng d
020 $a 9798535526096
035 $a (MiAaPQ)AAI28736222
035 $a (MiAaPQ)0187rice2255Weng
035 $a AAI28736222
040 $a MiAaPQ $c MiAaPQ
100 1 $a Weng, Sheng. $3 3688026
245 1 0 $a Integrating Coherent Anti-Stokes Raman Scattering Imaging and Deep Learning Analytics for High Precision, Real Time, Label Free Cancer Diagnosis.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 166 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-04, Section: B.
500 $a Advisor: Kelly, Kevin;Wong, Stephen T.C.
502 $a Thesis (Ph.D.)--Rice University, 2017.
506 $a This item must not be sold to any third party vendors.
520 $a Coherent anti-Stokes Raman scattering (CARS) imaging technique has demonstrated great potential in clinical diagnosis by providing cellular-level resolution images without using exogenous contrast agents. This thesis contributes to the formation of an optical fiber based signal collection scheme and an automated image analytics platform to translate CARS microscopy for clinical uses. First, I introduce the concept of CARS by showing original images acquired from thyroid and parathyroid tissues. Second, I describe the use of a customized optical fiber bundle to collect and differentiate forward and backward generated CARS signals that contain different structural information. Third, I demonstrate the feasibility of using deep learning algorithms to characterize and classify CARS images automatically. In particular, I apply transfer learning on the CARS images and achieve 89.2% prediction accuracy in differentiating normal, small-cell carcinoma, adenocarcinoma, and squamous cell carcinoma human lung images. The combination of an optical fiber based microendoscopy and deep learning image classification algorithm will facilitate CARS imaging for on-the-spot cancer diagnosis, allowing medical practitioners to obtain essential information in real time and accelerate clinical decision-making. Meanwhile, the thesis also shows the generality of the deep learning algorithm developed by classifying screening images generated in drug discovery. As an example, for automated classification of large volumes of high-content screening images for Alzheimer's disease drug discovery, by applying similar transfer learning method on hyperphosphorylated tau images, I categorize drug hits into ineffective, partially-effective, and significantly-effective groups with high speed and accuracy.
590 $a School code: 0187.
650 4 $a Statistics. $3 517247
650 4 $a Biomedical engineering. $3 535387
650 4 $a Optics. $3 517925
650 4 $a Artificial intelligence. $3 516317
650 4 $a Pathology. $3 643180
650 4 $a Medical imaging. $3 3172799
653 $a Nonlinear imaging
653 $a Biophotonics
653 $a Microscopy
653 $a Optical fibers
653 $a Image analysis
653 $a Deep learning
653 $a Cancer diagnosis
653 $a Drug screening
653 $a Coherent Anti-Stokes Raman Scattering
690 $a 0752
690 $a 0541
690 $a 0463
690 $a 0571
690 $a 0800
690 $a 0574
710 2 $a Rice University. $b Applied Physics. $3 2105391
773 0 $t Dissertations Abstracts International $g 83-04B.
790 $a 0187
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28736222