東華大學圖書館 |

Ophthalmic medical image analysis = 7th International Workshop, OMIA 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 8, 2020 : proceedings /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Ophthalmic medical image analysis/ edited by Huazhu Fu ... [et al.].
Reminder of title:	7th International Workshop, OMIA 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 8, 2020 : proceedings /
remainder title:	OMIA 2020
other author:	Fu, Huazhu.
corporate name:	OMIA (Workshop)
Published:	Cham :Springer International Publishing : : 2020.,
Description:	ix, 218 p. :ill., digital ;24 cm.
[NT 15003449]:	Bio-Inspired Attentive Segmentation of Retinal OCT imaging -- DR detection using Optical Coherence Tomography Angiography (OCTA): a transfer learning approach with robustness analysis -- What is the optimal attribution method for explainable ophthalmic disease classification? -- DeSupGAN: Multi-scale Feature Averaging Generative Adversarial Network for Simultaneous De-blurring and Super-resolution of Retinal Fundus Images -- Encoder-Decoder Networks for Retinal Vessel Segmentation using Large Multi-Scale Patches -- Retinal Image Quality Assessment via Specific Structures Segmentation -- Cascaded Attention Guided Network for Retinal Vessel Segmentation -- Self-supervised Denoising via Diffeomorphic Template Estimation: Application to Optical Coherence Tomography -- Automated Detection of Diabetic Retinopathy From Smartphone Fundus Videos -- Optic Disc, Cup and Fovea Detection from Retinal Images using U-Net++ with EfficientNet Encoder -- Multi-level Light U-Net and Atrous Spatial Pyramid Pooling for Optic Disc Segmentation on Fundus Image -- An Interactive Approach to Region of Interest Selection in Cytologic Analysis of Uveal Melanoma Based on Unsupervised Clustering -- Retinal OCT Denoising with Pseudo-Multimodal Fusion Network -- Deep-Learning-Based Estimation of 3D Optic-Nerve-Head Shape from 2D Color Fundus Photographs in Cases of Optic Disc Swelling -- Weakly supervised retinal detachment segmentation using deep feature propagation learning in SD-OCT images -- A framework for the discovery of retinal biomarkers in Optical Coherence Tomography Angiography (OCTA) -- An Automated Aggressive Posterior Retinopathy of Prematurity Diagnosis System by Squeeze and Excitation Hierarchical Bilinear Pooling Network -- Weakly-Supervised Lesion-aware and Consistency Regularization for Retinitis Pigmentosa Detection from Ultra-widefield Images -- A Conditional Generative Adversarial Network-based Method for Eye Fundus Image Quality Enhancement -- Construction of quantitative indexes for cataract surgery evaluation based on deep learning -- Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification.
Contained By:	Springer Nature eBook
Subject:	Pathology - Congresses. - Data processing -
Online resource:	https://doi.org/10.1007/978-3-030-63419-3
ISBN:	9783030634193

Ophthalmic medical image analysis = 7th International Workshop, OMIA 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 8, 2020 : proceedings /
Ophthalmic medical image analysis7th International Workshop, OMIA 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 8, 2020 : proceedings /[electronic resource] :OMIA 2020edited by Huazhu Fu ... [et al.]. - Cham :Springer International Publishing :2020. - ix, 218 p. :ill., digital ;24 cm. - Lecture notes in computer science,120690302-9743 ;. - Lecture notes in computer science ;12069..

Bio-Inspired Attentive Segmentation of Retinal OCT imaging -- DR detection using Optical Coherence Tomography Angiography (OCTA): a transfer learning approach with robustness analysis -- What is the optimal attribution method for explainable ophthalmic disease classification? -- DeSupGAN: Multi-scale Feature Averaging Generative Adversarial Network for Simultaneous De-blurring and Super-resolution of Retinal Fundus Images -- Encoder-Decoder Networks for Retinal Vessel Segmentation using Large Multi-Scale Patches -- Retinal Image Quality Assessment via Specific Structures Segmentation -- Cascaded Attention Guided Network for Retinal Vessel Segmentation -- Self-supervised Denoising via Diffeomorphic Template Estimation: Application to Optical Coherence Tomography -- Automated Detection of Diabetic Retinopathy From Smartphone Fundus Videos -- Optic Disc, Cup and Fovea Detection from Retinal Images using U-Net++ with EfficientNet Encoder -- Multi-level Light U-Net and Atrous Spatial Pyramid Pooling for Optic Disc Segmentation on Fundus Image -- An Interactive Approach to Region of Interest Selection in Cytologic Analysis of Uveal Melanoma Based on Unsupervised Clustering -- Retinal OCT Denoising with Pseudo-Multimodal Fusion Network -- Deep-Learning-Based Estimation of 3D Optic-Nerve-Head Shape from 2D Color Fundus Photographs in Cases of Optic Disc Swelling -- Weakly supervised retinal detachment segmentation using deep feature propagation learning in SD-OCT images -- A framework for the discovery of retinal biomarkers in Optical Coherence Tomography Angiography (OCTA) -- An Automated Aggressive Posterior Retinopathy of Prematurity Diagnosis System by Squeeze and Excitation Hierarchical Bilinear Pooling Network -- Weakly-Supervised Lesion-aware and Consistency Regularization for Retinitis Pigmentosa Detection from Ultra-widefield Images -- A Conditional Generative Adversarial Network-based Method for Eye Fundus Image Quality Enhancement -- Construction of quantitative indexes for cataract surgery evaluation based on deep learning -- Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification.

This book constitutes the refereed proceedings of the 6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2020, held in conjunction with the 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The workshop was held virtually due to the COVID-19 crisis. The 21 papers presented at OMIA 2020 were carefully reviewed and selected from 34 submissions. The papers cover various topics in the field of ophthalmic medical image analysis and challenges in terms of reliability and validation, number and type of conditions considered, multi-modal analysis (e.g., fundus, optical coherence tomography, scanning laser ophthalmoscopy), novel imaging technologies, and the effective transfer of advanced computer vision and machine learning technologies.

ISBN: 9783030634193

Standard No.: 10.1007/978-3-030-63419-3doiSubjects--Topical Terms:

3453586
Pathology
--Data processing--Congresses.

LC Class. No.: RB38 / .O55 2021

Dewey Class. No.: 616.07

Ophthalmic medical image analysis = 7th International Workshop, OMIA 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 8, 2020 : proceedings /
LDR:04327nmm a2200385 a 4500 001 2256935
003 DE-He213
005 20201119044536.0
006 m d
007 cr nn 008maaau
008 220420s2020 sz s 0 eng d
020 $a 9783030634193 $q (electronic bk.)
020 $a 9783030634186 $q (paper)
024 7 $a 10.1007/978-3-030-63419-3 $2 doi
035 $a 978-3-030-63419-3
040 $a GP $c GP
041 0 $a eng
050 4 $a RB38 $b .O55 2021
072 7 $a UYT $2 bicssc
072 7 $a COM012000 $2 bisacsh
072 7 $a UYT $2 thema
072 7 $a UYQV $2 thema
082 0 4 $a 616.07 $2 23
090 $a RB38 $b .O55 2021
111 2 $a OMIA (Workshop) $n (7th : $d 2020 : $c Online) $3 3527661
245 1 0 $a Ophthalmic medical image analysis $h [electronic resource] : $b 7th International Workshop, OMIA 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 8, 2020 : proceedings / $c edited by Huazhu Fu ... [et al.].
246 3 $a OMIA 2020
246 3 $a MICCAI 2020
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a ix, 218 p. : $b ill., digital ; $c 24 cm.
490 1 $a Lecture notes in computer science, $x 0302-9743 ; $v 12069
490 1 $a Image processing, computer vision, pattern recognition, and graphics
505 0 $a Bio-Inspired Attentive Segmentation of Retinal OCT imaging -- DR detection using Optical Coherence Tomography Angiography (OCTA): a transfer learning approach with robustness analysis -- What is the optimal attribution method for explainable ophthalmic disease classification? -- DeSupGAN: Multi-scale Feature Averaging Generative Adversarial Network for Simultaneous De-blurring and Super-resolution of Retinal Fundus Images -- Encoder-Decoder Networks for Retinal Vessel Segmentation using Large Multi-Scale Patches -- Retinal Image Quality Assessment via Specific Structures Segmentation -- Cascaded Attention Guided Network for Retinal Vessel Segmentation -- Self-supervised Denoising via Diffeomorphic Template Estimation: Application to Optical Coherence Tomography -- Automated Detection of Diabetic Retinopathy From Smartphone Fundus Videos -- Optic Disc, Cup and Fovea Detection from Retinal Images using U-Net++ with EfficientNet Encoder -- Multi-level Light U-Net and Atrous Spatial Pyramid Pooling for Optic Disc Segmentation on Fundus Image -- An Interactive Approach to Region of Interest Selection in Cytologic Analysis of Uveal Melanoma Based on Unsupervised Clustering -- Retinal OCT Denoising with Pseudo-Multimodal Fusion Network -- Deep-Learning-Based Estimation of 3D Optic-Nerve-Head Shape from 2D Color Fundus Photographs in Cases of Optic Disc Swelling -- Weakly supervised retinal detachment segmentation using deep feature propagation learning in SD-OCT images -- A framework for the discovery of retinal biomarkers in Optical Coherence Tomography Angiography (OCTA) -- An Automated Aggressive Posterior Retinopathy of Prematurity Diagnosis System by Squeeze and Excitation Hierarchical Bilinear Pooling Network -- Weakly-Supervised Lesion-aware and Consistency Regularization for Retinitis Pigmentosa Detection from Ultra-widefield Images -- A Conditional Generative Adversarial Network-based Method for Eye Fundus Image Quality Enhancement -- Construction of quantitative indexes for cataract surgery evaluation based on deep learning -- Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification.
520 $a This book constitutes the refereed proceedings of the 6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2020, held in conjunction with the 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The workshop was held virtually due to the COVID-19 crisis. The 21 papers presented at OMIA 2020 were carefully reviewed and selected from 34 submissions. The papers cover various topics in the field of ophthalmic medical image analysis and challenges in terms of reliability and validation, number and type of conditions considered, multi-modal analysis (e.g., fundus, optical coherence tomography, scanning laser ophthalmoscopy), novel imaging technologies, and the effective transfer of advanced computer vision and machine learning technologies.
650 0 $a Pathology $x Data processing $v Congresses. $3 3453586
650 0 $a Eye $x Imaging $v Congresses. $3 3453587
650 1 4 $a Image Processing and Computer Vision. $3 891070
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Pattern Recognition. $3 891045
650 2 4 $a Computer Systems Organization and Communication Networks. $3 891212
700 1 $a Fu, Huazhu. $3 3453584
710 2 $a SpringerLink (Online service) $3 836513
711 2 $a International Conference on Medical Image Computing and Computer-Assisted Intervention $n (23rd : $d 2020 : $c Online) $3 3492452
773 0 $t Springer Nature eBook
830 0 $a Lecture notes in computer science ; $v 12069. $3 3527662
830 0 $a Image processing, computer vision, pattern recognition, and graphics. $3 3382509
856 4 0 $u https://doi.org/10.1007/978-3-030-63419-3
950 $a Computer Science (SpringerNature-11645)