東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Ultra-widefield fundus imaging for d...

UWF4DR (Conference) (2024 :)

Linked to FindBook

Google Book

Amazon

博客來

Ultra-widefield fundus imaging for diabetic retinopathy = first MICCAI Challenge, UWF4DR 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024 : proceedings /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Ultra-widefield fundus imaging for diabetic retinopathy/ edited by Bin Sheng ... [et al.].
Reminder of title:	first MICCAI Challenge, UWF4DR 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024 : proceedings /
remainder title:	UWF4DR 2024
other author:	Sheng, Bin.
corporate name:	UWF4DR (Conference)
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	x, 176 p. :ill. (chiefly color), digital ;24 cm.
[NT 15003449]:	Image Quality Assessment with Model Fusion for Ultra-Widefield Fundus. -- AI Algorithm for Ultra-Widefield Fundus Imaging forDiabetic Retinopathy-RDR, DME. -- Lightweight and Accurate: ShuffleNet for Diabetic Retinopathy and EfficientNet for Diabetic Macular Edema Diagnosis. -- Efficient Deep Learning Models for Ultra-Widefield Fundus Imaging for Diabetic Retinopathy. -- Bag of Tricks for Ultra-widefield Fundus Image Quality Assessment. -- Bag of Tricks for Diabetic Retinopathy and Diabetic Macular Edema Classification in Ultra-Widefield Imaging. -- Deep Self-Supervised Learning for Ultra-Widefield Fundus Image Quality Assessment. -- Reliable DL-based Referable Diabetic Retinopathy and Diabetic Macular Edema Detection Using Ultra-Widefield Fundus Images. -- Deep Learning-Based Detection of Referable Diabetic Retinopathy and Macular Edema Using Ultra-Widefield Fundus Imaging. -- A Comprehensive Approach to Diabetic Retinopathy Classification: Combining ResNet34 with Enhanced Pre-processing for Ultra-Widefield Fundus Imaging. -- An ultra-efficient method for real-time ultra-widefield fundus image quality assessment. -- Ultra-fast detection of referable diabetic retinopathy and macular edema in ultra-widefield fundus imaging using a unified risk score. -- Efficient Deep Learning Approaches for Processing Ultra-Widefield Retinal Imaging. -- EfficientNet-B1 Based Diabetic Retinopathy Detection from Ultra-Widefield Fundus Images. -- Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification. -- DME-MobileNet: Fine-tuning nnMobileNet For Diabetic Macular Edema Classification. -- Automatic Identification Method for Diabetic Macular Edema in Ultra-Widefield Fundus Images.
Contained By:	Springer Nature eBook
Subject:	Diabetic retinopathy - Congresses. - Imaging -
Online resource:	https://doi.org/10.1007/978-3-031-89388-9
ISBN:	9783031893889

Ultra-widefield fundus imaging for diabetic retinopathy = first MICCAI Challenge, UWF4DR 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024 : proceedings /
Ultra-widefield fundus imaging for diabetic retinopathyfirst MICCAI Challenge, UWF4DR 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024 : proceedings /[electronic resource] :UWF4DR 2024edited by Bin Sheng ... [et al.]. - Cham :Springer Nature Switzerland :2025. - x, 176 p. :ill. (chiefly color), digital ;24 cm. - Lecture notes in computer science,155971611-3349 ;. - Lecture notes in computer science ;15597..

Image Quality Assessment with Model Fusion for Ultra-Widefield Fundus. -- AI Algorithm for Ultra-Widefield Fundus Imaging forDiabetic Retinopathy-RDR, DME. -- Lightweight and Accurate: ShuffleNet for Diabetic Retinopathy and EfficientNet for Diabetic Macular Edema Diagnosis. -- Efficient Deep Learning Models for Ultra-Widefield Fundus Imaging for Diabetic Retinopathy. -- Bag of Tricks for Ultra-widefield Fundus Image Quality Assessment. -- Bag of Tricks for Diabetic Retinopathy and Diabetic Macular Edema Classification in Ultra-Widefield Imaging. -- Deep Self-Supervised Learning for Ultra-Widefield Fundus Image Quality Assessment. -- Reliable DL-based Referable Diabetic Retinopathy and Diabetic Macular Edema Detection Using Ultra-Widefield Fundus Images. -- Deep Learning-Based Detection of Referable Diabetic Retinopathy and Macular Edema Using Ultra-Widefield Fundus Imaging. -- A Comprehensive Approach to Diabetic Retinopathy Classification: Combining ResNet34 with Enhanced Pre-processing for Ultra-Widefield Fundus Imaging. -- An ultra-efficient method for real-time ultra-widefield fundus image quality assessment. -- Ultra-fast detection of referable diabetic retinopathy and macular edema in ultra-widefield fundus imaging using a unified risk score. -- Efficient Deep Learning Approaches for Processing Ultra-Widefield Retinal Imaging. -- EfficientNet-B1 Based Diabetic Retinopathy Detection from Ultra-Widefield Fundus Images. -- Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification. -- DME-MobileNet: Fine-tuning nnMobileNet For Diabetic Macular Edema Classification. -- Automatic Identification Method for Diabetic Macular Edema in Ultra-Widefield Fundus Images.

This book constitutes the proceedings of the First MICCAI Challenge on Ultra-Widefield Fundus Imaging for Diabetic Retinopathy, UWF4DR 2024, held in Marrakesh, Morocco, on October 10, 2024. The 17 full papers included in this book were carefully reviewed and selected from 17 submissions. They present methodologies and results of the challenge which consists of three clinically relevant subtasks: image quality assessment for ultra-widefield fundus (Task 1), identification of referable diabetic retinopathy (Task 2), and identification of diabetic macular edema (Task 3).

ISBN: 9783031893889

Standard No.: 10.1007/978-3-031-89388-9doiSubjects--Topical Terms:

3783360
Diabetic retinopathy
--Imaging--Congresses.

LC Class. No.: RE661.D5

Dewey Class. No.: 617.735

Ultra-widefield fundus imaging for diabetic retinopathy = first MICCAI Challenge, UWF4DR 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024 : proceedings /
LDR:03538nmm a2200361 a 4500 001 2409855
003 DE-He213
005 20250513130327.0
006 m d
007 cr nn 008maaau
008 260204s2025 sz s 0 eng d
020 $a 9783031893889 $q (electronic bk.)
020 $a 9783031893872 $q (paper)
024 7 $a 10.1007/978-3-031-89388-9 $2 doi
035 $a 978-3-031-89388-9
040 $a GP $c GP
041 0 $a eng
050 4 $a RE661.D5
072 7 $a UYT $2 bicssc
072 7 $a COM016000 $2 bisacsh
072 7 $a UYT $2 thema
082 0 4 $a 617.735 $2 23
090 $a RE661.D5 $b U95 2024
111 2 $a UWF4DR (Conference) $n (1st : $d 2024 : $c Marrakech, Morocco) $3 3783358
245 1 0 $a Ultra-widefield fundus imaging for diabetic retinopathy $h [electronic resource] : $b first MICCAI Challenge, UWF4DR 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024 : proceedings / $c edited by Bin Sheng ... [et al.].
246 3 $a UWF4DR 2024
246 3 $a MICCAI 2024
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a x, 176 p. : $b ill. (chiefly color), digital ; $c 24 cm.
490 1 $a Lecture notes in computer science, $x 1611-3349 ; $v 15597
505 0 $a Image Quality Assessment with Model Fusion for Ultra-Widefield Fundus. -- AI Algorithm for Ultra-Widefield Fundus Imaging forDiabetic Retinopathy-RDR, DME. -- Lightweight and Accurate: ShuffleNet for Diabetic Retinopathy and EfficientNet for Diabetic Macular Edema Diagnosis. -- Efficient Deep Learning Models for Ultra-Widefield Fundus Imaging for Diabetic Retinopathy. -- Bag of Tricks for Ultra-widefield Fundus Image Quality Assessment. -- Bag of Tricks for Diabetic Retinopathy and Diabetic Macular Edema Classification in Ultra-Widefield Imaging. -- Deep Self-Supervised Learning for Ultra-Widefield Fundus Image Quality Assessment. -- Reliable DL-based Referable Diabetic Retinopathy and Diabetic Macular Edema Detection Using Ultra-Widefield Fundus Images. -- Deep Learning-Based Detection of Referable Diabetic Retinopathy and Macular Edema Using Ultra-Widefield Fundus Imaging. -- A Comprehensive Approach to Diabetic Retinopathy Classification: Combining ResNet34 with Enhanced Pre-processing for Ultra-Widefield Fundus Imaging. -- An ultra-efficient method for real-time ultra-widefield fundus image quality assessment. -- Ultra-fast detection of referable diabetic retinopathy and macular edema in ultra-widefield fundus imaging using a unified risk score. -- Efficient Deep Learning Approaches for Processing Ultra-Widefield Retinal Imaging. -- EfficientNet-B1 Based Diabetic Retinopathy Detection from Ultra-Widefield Fundus Images. -- Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification. -- DME-MobileNet: Fine-tuning nnMobileNet For Diabetic Macular Edema Classification. -- Automatic Identification Method for Diabetic Macular Edema in Ultra-Widefield Fundus Images.
520 $a This book constitutes the proceedings of the First MICCAI Challenge on Ultra-Widefield Fundus Imaging for Diabetic Retinopathy, UWF4DR 2024, held in Marrakesh, Morocco, on October 10, 2024. The 17 full papers included in this book were carefully reviewed and selected from 17 submissions. They present methodologies and results of the challenge which consists of three clinically relevant subtasks: image quality assessment for ultra-widefield fundus (Task 1), identification of referable diabetic retinopathy (Task 2), and identification of diabetic macular edema (Task 3).
650 0 $a Diabetic retinopathy $x Imaging $v Congresses. $3 3783360
650 0 $a Retina $x Imaging $v Congresses. $3 3783361
650 1 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 890871
700 1 $a Sheng, Bin. $3 3633729
710 2 $a SpringerLink (Online service) $3 836513
711 2 $a International Conference on Medical Image Computing and Computer-Assisted Intervention $n (27th : $d 2024 : $c Marrakech, Morocco) $3 3724079
773 0 $t Springer Nature eBook
830 0 $a Lecture notes in computer science ; $v 15597. $3 3783359
856 4 0 $u https://doi.org/10.1007/978-3-031-89388-9
950 $a Computer Science (SpringerNature-11645)