找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問(wèn)微社區(qū)

打印 上一主題 下一主題

Titlebook: GANs for Data Augmentation in Healthcare; Arun Solanki,Mohd Naved Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusi

[復(fù)制鏈接]
查看: 25034|回復(fù): 56
樓主
發(fā)表于 2025-3-21 19:10:06 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱GANs for Data Augmentation in Healthcare
編輯Arun Solanki,Mohd Naved
視頻videohttp://file.papertrans.cn/381/380055/380055.mp4
概述Oriented towards the applications and not just the theory.Contains work from some of the pioneers of GAN.Covers practical aspects with possible supported results
圖書封面Titlebook: GANs for Data Augmentation in Healthcare;  Arun Solanki,Mohd Naved Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusi
描述.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 are often different because of the cost of obtaining information and the time spent consuming the information. In general, clinical data is unreliable and therefore 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 an 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
出版日期Book 2023
關(guān)鍵詞GANS; Healthcare; Machine Learning; Medical Records; Generative Adversarial Network; GAN based Image Augm
版次1
doihttps://doi.org/10.1007/978-3-031-43205-7
isbn_softcover978-3-031-43207-1
isbn_ebook978-3-031-43205-7
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

書目名稱GANs for Data Augmentation in Healthcare影響因子(影響力)




書目名稱GANs for Data Augmentation in Healthcare影響因子(影響力)學(xué)科排名




書目名稱GANs for Data Augmentation in Healthcare網(wǎng)絡(luò)公開度




書目名稱GANs for Data Augmentation in Healthcare網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱GANs for Data Augmentation in Healthcare被引頻次




書目名稱GANs for Data Augmentation in Healthcare被引頻次學(xué)科排名




書目名稱GANs for Data Augmentation in Healthcare年度引用




書目名稱GANs for Data Augmentation in Healthcare年度引用學(xué)科排名




書目名稱GANs for Data Augmentation in Healthcare讀者反饋




書目名稱GANs for Data Augmentation in Healthcare讀者反饋學(xué)科排名




單選投票, 共有 1 人參與投票
 

1票 100.00%

Perfect with Aesthetics

 

0票 0.00%

Better Implies Difficulty

 

0票 0.00%

Good and Satisfactory

 

0票 0.00%

Adverse Performance

 

0票 0.00%

Disdainful Garbage

您所在的用戶組沒(méi)有投票權(quán)限
沙發(fā)
發(fā)表于 2025-3-21 20:18:06 | 只看該作者
,A Review on Mode Collapse Reducing GANs with GAN’s Algorithm and Theory, together with generator. Generator generates the data which resembles the actual data and discriminator differentiates between actual data and generated data. Due to GAN’s complex structure, it becomes very hard to train it and it faces a lot of problems. Among these problems mode collapse is a ver
板凳
發(fā)表于 2025-3-22 00:33:33 | 只看該作者
Medical Image Synthesis Using Generative Adversarial Networks,phthalmology analysis of retinal networks gives information about the status and health condition of the eyes. The entire visual system is threatened by retinal illnesses such as retinal artery and vein occlusion, which can be prevented with early detection. Many supervised and unsupervised practice
地板
發(fā)表于 2025-3-22 06:08:38 | 只看該作者
5#
發(fā)表于 2025-3-22 09:57:48 | 只看該作者
State of the Art Framework-Based Detection of GAN-Generated Face Images,hesis tasks. GANs have had great success in replicating real data distributions, especially images, which has led to a large amount of research on the same. More false face photos are being shared online thanks to the growth of face image transformation methods that use GANs. Automated methods to re
6#
發(fā)表于 2025-3-22 13:39:03 | 只看該作者
Data Augmentation Approaches Using Cycle Consistent Adversarial Networks,ficient amount of data for the model to learn efficiently. For this reason several data augmentation approaches have been introduced. Generative Adversarial Networks (GANs) are unsupervised generative models that have this power. These models are used to generate new instances of data by identifying
7#
發(fā)表于 2025-3-22 20:12:35 | 只看該作者
Geometric Transformations-Based Medical Image Augmentation,that both ML and DL algorithms are capable of identifying links between enormous amounts of data, one of the jobs for which these techniques have the most potential is visual inspection. These methods, nevertheless, call for a lot of photographs, which are not always possible to capture. Techniques
8#
發(fā)表于 2025-3-22 21:52:56 | 只看該作者
9#
發(fā)表于 2025-3-23 03:06:42 | 只看該作者
10#
發(fā)表于 2025-3-23 08:14:47 | 只看該作者
Combining Super-Resolution GAN and DC GAN for Enhancing Medical Image Generation: A Study on ImprovPatient Version). The two main layers of the skin are the dermis (the lower or inner layer) and the epidermis (the higher or outer layer) (Donaldson, 2022). The most typical cancer in the world is skin cancer, which is becoming more frequent (Shao et al., 2017). The three types of cancers are basal
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評(píng) 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國(guó)際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-10 02:46
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
柞水县| 抚顺市| 庆城县| 海口市| 交城县| 聂拉木县| 西贡区| 绥化市| 鄂州市| 丹巴县| 山丹县| 张掖市| 勐海县| 田东县| 东方市| 崇仁县| 淮阳县| 望都县| 奇台县| 长宁县| 临沭县| 西乌珠穆沁旗| 朝阳区| 芦山县| 湾仔区| 宣城市| 道真| 通山县| 峨边| 磐安县| 苗栗县| 东丽区| 洪洞县| 增城市| 酒泉市| 米林县| 南皮县| 普陀区| 雷州市| 确山县| 九台市|