語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
到查詢結果
[ subject:"Diagnosis- Data processing." ]
切換:
標籤
|
MARC模式
|
ISBD
Gans for data augmentation in healthcare
~
Solanki, Arun.
FindBook
Google Book
Amazon
博客來
Gans for data augmentation in healthcare
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Gans for data augmentation in healthcare/ edited by Arun Solanki, Mohd Naved.
其他作者:
Solanki, Arun.
出版者:
Cham :Springer International Publishing : : 2023.,
面頁冊數:
x, 251 p. :illustrations (some col.), digital ;24 cm.
內容註:
Chapter. 1. Role of Machine learning in Detection and Classification of Leukemia: A Comparative Analysis -- Chapter. 2. A Review on Mode Collapse Reducing GANs with GAN's Algorithm and Theory -- Chapter. 3. Medical Image Synthesis using Generative Adversarial Networks -- Chapter. 4. Chest X-ray data augmentation with Generative Adversarial Networks for pneumonia and COVID diagnosis -- Chapter. 5. State of the Art Framework based Detection of GAN Generated Face Images -- Chapter. 6. Data Augmentation in Classifying Chest Radiograph Images (CXR) using DCGAN-CNN -- Chapter. 7. Data Augmentation Approaches Using Cycle Consistent Adversarial Networks -- Chapter. 8. Geometric Transformations-based Medical Image Augmentation -- Chapter. 9. Generative Adversarial Learning for Medical Thermal Imaging Analysis -- Chapter. 10. Improving Performance of a Brain Tumor Detection on MRI Images using DCGAN-based Data Augmentation and Vision Transformer (ViT) Approach -- Chapter. 11. Combining Super-Resolution GAN and DC GAN for Enhancing Medical Image Generation: A Study on Improving CNN Model Performance -- Chapter. 12. GAN for Augmenting Cardiac MRI Segmentation -- Chapter. 13. WGAN for Data Augmentation -- Chapter. 14. Image Segmentation in Medical Images by Using Semi - Supervised Methods.
Contained By:
Springer Nature eBook
標題:
Diagnosis - Data processing. -
電子資源:
https://doi.org/10.1007/978-3-031-43205-7
ISBN:
9783031432057
Gans for data augmentation in healthcare
Gans for data augmentation in healthcare
[electronic resource] /edited by Arun Solanki, Mohd Naved. - Cham :Springer International Publishing :2023. - x, 251 p. :illustrations (some col.), digital ;24 cm.
Chapter. 1. Role of Machine learning in Detection and Classification of Leukemia: A Comparative Analysis -- Chapter. 2. A Review on Mode Collapse Reducing GANs with GAN's Algorithm and Theory -- Chapter. 3. Medical Image Synthesis using Generative Adversarial Networks -- Chapter. 4. Chest X-ray data augmentation with Generative Adversarial Networks for pneumonia and COVID diagnosis -- Chapter. 5. State of the Art Framework based Detection of GAN Generated Face Images -- Chapter. 6. Data Augmentation in Classifying Chest Radiograph Images (CXR) using DCGAN-CNN -- Chapter. 7. Data Augmentation Approaches Using Cycle Consistent Adversarial Networks -- Chapter. 8. Geometric Transformations-based Medical Image Augmentation -- Chapter. 9. Generative Adversarial Learning for Medical Thermal Imaging Analysis -- Chapter. 10. Improving Performance of a Brain Tumor Detection on MRI Images using DCGAN-based Data Augmentation and Vision Transformer (ViT) Approach -- Chapter. 11. Combining Super-Resolution GAN and DC GAN for Enhancing Medical Image Generation: A Study on Improving CNN Model Performance -- Chapter. 12. GAN for Augmenting Cardiac MRI Segmentation -- Chapter. 13. WGAN for Data Augmentation -- Chapter. 14. Image Segmentation in Medical Images by Using Semi - Supervised Methods.
Computer-Assisted Diagnostics (CAD) using Convolutional Neural Network (CNN) model has become an important technology in the medical industry, improving the accuracy of diagnostics. However, the lack Magnetic Resonance Imaging (MRI) data leads to the failure of the depth study algorithm. Medical records often different because of the cost of obtaining information and the time-consuming information. In general, clinical data are unreliable, the training of neural network methods to distribute disease across classes does not yield the desired results. Data augmentation is often done by training data to solve problems caused by augmentation tasks such as scaling, cropping, flipping, padding, rotation, translation, affine transformation, and color augmentation techniques such as brightness, contrast, saturation, and hue.Data Augmentation and Segmentation imaging using GAN can be used to provide clear images of brain, liver, chest, abdomen, and liver on MRI. In addition, GAN shows strong promise in the field of clinical image synthesis. In many cases, clinical evaluation is limited by a lack of data and/or the cost of actual information. GAN can overcome these problems by enabling scientists and clinicians to work on beautiful and realistic images. This can improve diagnosis, prognosis, and disease. Finally, GAN highlights the potential for location of patient information with data. This is a beneficial clinical application of GAN because it can effectively protect patient confidentiality. The proposed book covers the application of GANs on medical imaging augmentation and segmentation.
ISBN: 9783031432057
Standard No.: 10.1007/978-3-031-43205-7doiSubjects--Topical Terms:
1966311
Diagnosis
--Data processing.
LC Class. No.: RC78.7.D35
Dewey Class. No.: 616.07540285
Gans for data augmentation in healthcare
LDR
:03919nmm a2200337 a 4500
001
2336308
003
DE-He213
005
20231113204901.0
006
m d
007
cr nn 008maaau
008
240402s2023 sz s 0 eng d
020
$a
9783031432057
$q
(electronic bk.)
020
$a
9783031432040
$q
(paper)
024
7
$a
10.1007/978-3-031-43205-7
$2
doi
035
$a
978-3-031-43205-7
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
RC78.7.D35
072
7
$a
MBG
$2
bicssc
072
7
$a
UB
$2
bicssc
072
7
$a
MED000000
$2
bisacsh
072
7
$a
UXT
$2
thema
082
0 4
$a
616.07540285
$2
23
090
$a
RC78.7.D35
$b
G199 2023
245
0 0
$a
Gans for data augmentation in healthcare
$h
[electronic resource] /
$c
edited by Arun Solanki, Mohd Naved.
260
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2023.
300
$a
x, 251 p. :
$b
illustrations (some col.), digital ;
$c
24 cm.
505
0
$a
Chapter. 1. Role of Machine learning in Detection and Classification of Leukemia: A Comparative Analysis -- Chapter. 2. A Review on Mode Collapse Reducing GANs with GAN's Algorithm and Theory -- Chapter. 3. Medical Image Synthesis using Generative Adversarial Networks -- Chapter. 4. Chest X-ray data augmentation with Generative Adversarial Networks for pneumonia and COVID diagnosis -- Chapter. 5. State of the Art Framework based Detection of GAN Generated Face Images -- Chapter. 6. Data Augmentation in Classifying Chest Radiograph Images (CXR) using DCGAN-CNN -- Chapter. 7. Data Augmentation Approaches Using Cycle Consistent Adversarial Networks -- Chapter. 8. Geometric Transformations-based Medical Image Augmentation -- Chapter. 9. Generative Adversarial Learning for Medical Thermal Imaging Analysis -- Chapter. 10. Improving Performance of a Brain Tumor Detection on MRI Images using DCGAN-based Data Augmentation and Vision Transformer (ViT) Approach -- Chapter. 11. Combining Super-Resolution GAN and DC GAN for Enhancing Medical Image Generation: A Study on Improving CNN Model Performance -- Chapter. 12. GAN for Augmenting Cardiac MRI Segmentation -- Chapter. 13. WGAN for Data Augmentation -- Chapter. 14. Image Segmentation in Medical Images by Using Semi - Supervised Methods.
520
$a
Computer-Assisted Diagnostics (CAD) using Convolutional Neural Network (CNN) model has become an important technology in the medical industry, improving the accuracy of diagnostics. However, the lack Magnetic Resonance Imaging (MRI) data leads to the failure of the depth study algorithm. Medical records often different because of the cost of obtaining information and the time-consuming information. In general, clinical data are unreliable, the training of neural network methods to distribute disease across classes does not yield the desired results. Data augmentation is often done by training data to solve problems caused by augmentation tasks such as scaling, cropping, flipping, padding, rotation, translation, affine transformation, and color augmentation techniques such as brightness, contrast, saturation, and hue.Data Augmentation and Segmentation imaging using GAN can be used to provide clear images of brain, liver, chest, abdomen, and liver on MRI. In addition, GAN shows strong promise in the field of clinical image synthesis. In many cases, clinical evaluation is limited by a lack of data and/or the cost of actual information. GAN can overcome these problems by enabling scientists and clinicians to work on beautiful and realistic images. This can improve diagnosis, prognosis, and disease. Finally, GAN highlights the potential for location of patient information with data. This is a beneficial clinical application of GAN because it can effectively protect patient confidentiality. The proposed book covers the application of GANs on medical imaging augmentation and segmentation.
650
0
$a
Diagnosis
$x
Data processing.
$3
1966311
650
0
$a
Artificial intelligence
$x
Medical applications.
$3
900591
650
1 4
$a
Health Informatics.
$3
892928
650
2 4
$a
Image Processing.
$3
891209
650
2 4
$a
Machine Learning.
$3
3382522
700
1
$a
Solanki, Arun.
$3
3500864
700
1
$a
Naved, Mohd.
$3
3628360
710
2
$a
SpringerLink (Online service)
$3
836513
773
0
$t
Springer Nature eBook
856
4 0
$u
https://doi.org/10.1007/978-3-031-43205-7
950
$a
Medicine (SpringerNature-11650)
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
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
W9462513
電子資源
11.線上閱覽_V
電子書
EB RC78.7.D35
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入