國立虎尾科技大學 |

Ophthalmic Medical Image Analysis = 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Ophthalmic Medical Image Analysis/ edited by Huazhu Fu, Mona K. Garvin, Tom MacGillivray, Yanwu Xu, Yalin Zheng.
其他題名:	7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings /
其他作者:	Fu, Huazhu.
面頁冊數:	IX, 218 p. 19 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Optical data processing. -
電子資源:	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] :edited by Huazhu Fu, Mona K. Garvin, Tom MacGillivray, Yanwu Xu, Yalin Zheng. - 1st ed. 2020. - IX, 218 p. 19 illus.online resource. - Image Processing, Computer Vision, Pattern Recognition, and Graphics ;12069. - Image Processing, Computer Vision, Pattern Recognition, and Graphics ;9219.

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:

639187
Optical data processing.

LC Class. No.: TA1630-1650

Dewey Class. No.: 006.6

Ophthalmic Medical Image Analysis = 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings /
LDR:04616nam a22004335i 4500 001 1027145
003 DE-He213
005 20201119044536.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030634193 $9 978-3-030-63419-3
024 7 $a 10.1007/978-3-030-63419-3 $2 doi
035 $a 978-3-030-63419-3
050 4 $a TA1630-1650
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 006.6 $2 23
082 0 4 $a 006.37 $2 23
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, Mona K. Garvin, Tom MacGillivray, Yanwu Xu, Yalin Zheng.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a IX, 218 p. 19 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Image Processing, Computer Vision, Pattern Recognition, and Graphics ; $v 12069
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 Optical data processing. $3 639187
650 0 $a Artificial intelligence. $3 559380
650 0 $a Pattern recognition. $3 1253525
650 0 $a Computer organization. $3 596298
650 1 4 $a Image Processing and Computer Vision. $3 670819
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Pattern Recognition. $3 669796
650 2 4 $a Computer Systems Organization and Communication Networks. $3 669309
700 1 $a Fu, Huazhu. $e editor. $1 https://orcid.org/0000-0002-9702-5524 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1297542
700 1 $a Garvin, Mona K. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1282891
700 1 $a MacGillivray, Tom. $e editor. $1 https://orcid.org/0000-0001-5120-0086 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1297543
700 1 $a Xu, Yanwu. $e editor. $1 https://orcid.org/0000-0002-1779-931X $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1282884
700 1 $a Zheng, Yalin. $e editor. $1 https://orcid.org/0000-0002-7873-0922 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1297544
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030634186
776 0 8 $i Printed edition: $z 9783030634209
830 0 $a Image Processing, Computer Vision, Pattern Recognition, and Graphics ; $v 9219 $3 1253644
856 4 0 $u https://doi.org/10.1007/978-3-030-63419-3
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
912 $a ZDB-2-LNC
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)