語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Lung Nodule Detection via Deep Reinf...
~
Ali, Issa.
Lung Nodule Detection via Deep Reinforcement Learning.
紀錄類型:
書目-語言資料,手稿 : Monograph/item
正題名/作者:
Lung Nodule Detection via Deep Reinforcement Learning./
作者:
Ali, Issa.
面頁冊數:
1 online resource (19 pages)
附註:
Source: Masters Abstracts International, Volume: 57-05.
Contained By:
Masters Abstracts International57-05(E).
標題:
Public health. -
電子資源:
click for full text (PQDT)
ISBN:
9780355912852
Lung Nodule Detection via Deep Reinforcement Learning.
Ali, Issa.
Lung Nodule Detection via Deep Reinforcement Learning.
- 1 online resource (19 pages)
Source: Masters Abstracts International, Volume: 57-05.
Thesis (M.P.H.)--Yale University, 2018.
Includes bibliographical references
Lung cancer is the most common cause of cancer-related death globally. As a preventive measure, the United States Preventive Services Task Force (USPSTF) recommends annual screening of high risk individuals with low-dose computed tomography (CT). The resulting volume of CT scans from millions of people will pose a significant challenge for radiologists to interpret. To fill this gap, computer-aided detection (CAD) algorithms may prove to be the most promising solution. A crucial first step in the analysis of lung cancer screening results using CAD is the detection of pulmonary nodules, which may represent early-stage lung cancer. The objective of this work is to develop and validate a reinforcement learning model based on deep artificial neural networks for early detection of lung nodules in thoracic CT images. Inspired by the AlphaGo system, our deep learning algorithm takes a raw CT image as input and views it as a collection of states, and output a classification of whether a nodule is present or not. The dataset used to train our model is the LIDC/IDRI database hosted by the lung nodule analysis (LUNA) challenge. In total, there are 888 CT scans with annotations based on agreement from at least three out of four radiologists. As a result, there are 590 individuals having one or more nodules, and 298 having none. Our training results yielded an overall accuracy of 99.1% [sensitivity 99.2%, specificity 99.1%, positive predictive value (PPV) 99.1%, negative predictive value (NPV) 99.2%]. In our test, the results yielded an overall accuracy of 64.4% (sensitivity 58.9%, specificity 55.3%, PPV 54.2%, and NPV 60.0%). These early results show promise in solving the major issue of false positives in CT screening of lung nodules, and may help to save unnecessary follow-up tests and expenditures.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018
Mode of access: World Wide Web
ISBN: 9780355912852Subjects--Topical Terms:
560998
Public health.
Index Terms--Genre/Form:
554714
Electronic books.
Lung Nodule Detection via Deep Reinforcement Learning.
LDR
:03021ntm a2200349Ki 4500
001
916903
005
20180928111503.5
006
m o u
007
cr mn||||a|a||
008
190606s2018 xx obm 000 0 eng d
020
$a
9780355912852
035
$a
(MiAaPQ)AAI10809414
035
$a
(MiAaPQ)publichealthyale:10693
035
$a
AAI10809414
040
$a
MiAaPQ
$b
eng
$c
MiAaPQ
$d
NTU
100
1
$a
Ali, Issa.
$3
1190769
245
1 0
$a
Lung Nodule Detection via Deep Reinforcement Learning.
264
0
$c
2018
300
$a
1 online resource (19 pages)
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
500
$a
Source: Masters Abstracts International, Volume: 57-05.
500
$a
Adviser: Xiaomei Ma.
502
$a
Thesis (M.P.H.)--Yale University, 2018.
504
$a
Includes bibliographical references
520
$a
Lung cancer is the most common cause of cancer-related death globally. As a preventive measure, the United States Preventive Services Task Force (USPSTF) recommends annual screening of high risk individuals with low-dose computed tomography (CT). The resulting volume of CT scans from millions of people will pose a significant challenge for radiologists to interpret. To fill this gap, computer-aided detection (CAD) algorithms may prove to be the most promising solution. A crucial first step in the analysis of lung cancer screening results using CAD is the detection of pulmonary nodules, which may represent early-stage lung cancer. The objective of this work is to develop and validate a reinforcement learning model based on deep artificial neural networks for early detection of lung nodules in thoracic CT images. Inspired by the AlphaGo system, our deep learning algorithm takes a raw CT image as input and views it as a collection of states, and output a classification of whether a nodule is present or not. The dataset used to train our model is the LIDC/IDRI database hosted by the lung nodule analysis (LUNA) challenge. In total, there are 888 CT scans with annotations based on agreement from at least three out of four radiologists. As a result, there are 590 individuals having one or more nodules, and 298 having none. Our training results yielded an overall accuracy of 99.1% [sensitivity 99.2%, specificity 99.1%, positive predictive value (PPV) 99.1%, negative predictive value (NPV) 99.2%]. In our test, the results yielded an overall accuracy of 64.4% (sensitivity 58.9%, specificity 55.3%, PPV 54.2%, and NPV 60.0%). These early results show promise in solving the major issue of false positives in CT screening of lung nodules, and may help to save unnecessary follow-up tests and expenditures.
533
$a
Electronic reproduction.
$b
Ann Arbor, Mich. :
$c
ProQuest,
$d
2018
538
$a
Mode of access: World Wide Web
650
4
$a
Public health.
$3
560998
650
4
$a
Computer science.
$3
573171
650
4
$a
Artificial intelligence.
$3
559380
655
7
$a
Electronic books.
$2
local
$3
554714
690
$a
0573
690
$a
0984
690
$a
0800
710
2
$a
ProQuest Information and Learning Co.
$3
1178819
710
2
$a
Yale University.
$b
School of Public Health.
$3
1188289
773
0
$t
Masters Abstracts International
$g
57-05(E).
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10809414
$z
click for full text (PQDT)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入