語系:
繁體中文
English
說明(常見問題)
圖資館首頁
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Deep learners and deep learner descr...
~
Nanni, Loris.
Deep learners and deep learner descriptors for medical applications
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Deep learners and deep learner descriptors for medical applicationsedited by Loris Nanni ... [et al.].
其他作者:
Nanni, Loris.
出版者:
Cham :Springer International Publishing :2020.
面頁冊數:
xi, 284 p. :ill., digital ;24 cm.
Contained By:
Springer eBooks
標題:
Machine learning.
電子資源:
https://doi.org/10.1007/978-3-030-42750-4
ISBN:
9783030427504$q(electronic bk.)
Deep learners and deep learner descriptors for medical applications
Deep learners and deep learner descriptors for medical applications
[electronic resource] /edited by Loris Nanni ... [et al.]. - Cham :Springer International Publishing :2020. - xi, 284 p. :ill., digital ;24 cm. - Intelligent systems reference library,v.1861868-4394 ;. - Intelligent systems reference library ;v.24..
This book introduces readers to the current trends in using deep learners and deep learner descriptors for medical applications. It reviews the recent literature and presents a variety of medical image and sound applications to illustrate the five major ways deep learners can be utilized: 1) by training a deep learner from scratch (chapters provide tips for handling imbalances and other problems with the medical data); 2) by implementing transfer learning from a pre-trained deep learner and extracting deep features for different CNN layers that can be fed into simpler classifiers, such as the support vector machine; 3) by fine-tuning one or more pre-trained deep learners on an unrelated dataset so that they are able to identify novel medical datasets; 4) by fusing different deep learner architectures; and 5) by combining the above methods to generate a variety of more elaborate ensembles. This book is a value resource for anyone involved in engineering deep learners for medical applications as well as to those interested in learning more about the current techniques in this exciting field. A number of chapters provide source code that can be used to investigate topics further or to kick-start new projects.
ISBN: 9783030427504$q(electronic bk.)
Standard No.: 10.1007/978-3-030-42750-4doiSubjects--Topical Terms:
188639
Machine learning.
LC Class. No.: Q325.5 / .D447 2020
Dewey Class. No.: 006.31
Deep learners and deep learner descriptors for medical applications
LDR
:02235nmm a2200325 a 4500
001
579757
003
DE-He213
005
20201005132754.0
006
m
007
cr
008
201229s2020
020
$a
9783030427504$q(electronic bk.)
020
$a
9783030427481$q(paper)
024
7
$a
10.1007/978-3-030-42750-4
$2
doi
035
$a
978-3-030-42750-4
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
Q325.5
$b
.D447 2020
072
7
$a
UYQ
$2
bicssc
072
7
$a
TEC009000
$2
bisacsh
072
7
$a
UYQ
$2
thema
082
0 4
$a
006.31
$2
23
090
$a
Q325.5
$b
.D311 2020
245
0 0
$a
Deep learners and deep learner descriptors for medical applications
$h
[electronic resource] /
$c
edited by Loris Nanni ... [et al.].
260
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2020.
300
$a
xi, 284 p. :
$b
ill., digital ;
$c
24 cm.
490
1
$a
Intelligent systems reference library,
$x
1868-4394 ;
$v
v.186
520
$a
This book introduces readers to the current trends in using deep learners and deep learner descriptors for medical applications. It reviews the recent literature and presents a variety of medical image and sound applications to illustrate the five major ways deep learners can be utilized: 1) by training a deep learner from scratch (chapters provide tips for handling imbalances and other problems with the medical data); 2) by implementing transfer learning from a pre-trained deep learner and extracting deep features for different CNN layers that can be fed into simpler classifiers, such as the support vector machine; 3) by fine-tuning one or more pre-trained deep learners on an unrelated dataset so that they are able to identify novel medical datasets; 4) by fusing different deep learner architectures; and 5) by combining the above methods to generate a variety of more elaborate ensembles. This book is a value resource for anyone involved in engineering deep learners for medical applications as well as to those interested in learning more about the current techniques in this exciting field. A number of chapters provide source code that can be used to investigate topics further or to kick-start new projects.
650
0
$a
Machine learning.
$3
188639
650
0
$a
Artificial intelligence
$x
Medical applications.
$3
237917
650
1 4
$a
Computational Intelligence.
$3
338479
650
2 4
$a
Artificial Intelligence.
$3
212515
650
2 4
$a
Health Informatics.
$3
274212
650
2 4
$a
Biomedical Engineering and Bioengineering.
$3
826326
700
1
$a
Nanni, Loris.
$3
869207
710
2
$a
SpringerLink (Online service)
$3
273601
773
0
$t
Springer eBooks
830
0
$a
Intelligent systems reference library ;
$v
v.24.
$3
558591
856
4 0
$u
https://doi.org/10.1007/978-3-030-42750-4
950
$a
Intelligent Technologies and Robotics (Springer-42732)
筆 0 讀者評論
全部
電子館藏
館藏
1 筆 • 頁數 1 •
1
條碼號
館藏地
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
000000184343
電子館藏
1圖書
電子書
EB Q325.5 .D311 2020 2020
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
多媒體檔案
https://doi.org/10.1007/978-3-030-42750-4
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼
登入