Ultra-Widefield Fundus Imaging for Diabetic Retinopathy : First MICCAI Challenge, UWF4DR 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings / edited by Bin Sheng, Hao Chen, Tien Yin Wong, Carol Y. Cheung, Bo Qian.

Format:

Book

Contributor:

Sheng, Bin, Editor.

Chen, Hao., Editor.

Wong, Tien Yin, Editor.

Cheung, Carol Y., Editor.

Qian, Bo., Editor.

Series:

Lecture Notes in Computer Science, 1611-3349 ; 15597

Language:

English

Subjects (All):

Image processing--Digital techniques.

Image processing.

Computer vision.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Local Subjects:

Computer Imaging, Vision, Pattern Recognition and Graphics.

Physical Description:

1 online resource (X, 176 p. 64 illus., 63 illus. in color.)

Edition:

1st ed. 2025.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2025.

Summary:

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).

Contents:

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.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

3-031-89388-3

OCLC:

1524425095