LEADER 05367nam 22006735 450 001 9910427670103321 005 20251107172139.0 010 $a3-030-63419-1 024 7 $a10.1007/978-3-030-63419-3 035 $a(CKB)4100000011586075 035 $a(DE-He213)978-3-030-63419-3 035 $a(MiAaPQ)EBC6403594 035 $a(PPN)25250688X 035 $a(EXLCZ)994100000011586075 100 $a20201119d2020 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aOphthalmic Medical Image Analysis $e7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings /$fedited by Huazhu Fu, Mona K. Garvin, Tom MacGillivray, Yanwu Xu, Yalin Zheng 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (IX, 218 p.) $c19 illus 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v12069 300 $a"The workshop was held virtually due to the COVID-19 crisis." 300 $aIncludes author index. 311 0 $a3-030-63418-3 327 $aBio-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 Poolingfor 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. 330 $aThis book constitutes the refereed proceedings of the 6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2020, held in conjunction with the 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The workshop was held virtually due to the COVID-19 crisis. The 21 papers presented at OMIA 2020 were carefully reviewed and selected from 34 submissions. The papers cover various topics in the field of ophthalmic medical image analysis and challenges in terms of reliability and validation, number and type of conditions considered, multi-modal analysis (e.g., fundus, optical coherence tomography, scanning laser ophthalmoscopy), novel imaging technologies, and the effective transfer of advanced computer vision and machine learning technologies. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v12069 606 $aComputer vision 606 $aArtificial intelligence 606 $aPattern recognition systems 606 $aComputer engineering 606 $aComputer networks 606 $aComputer Vision 606 $aArtificial Intelligence 606 $aAutomated Pattern Recognition 606 $aComputer Engineering and Networks 615 0$aComputer vision. 615 0$aArtificial intelligence. 615 0$aPattern recognition systems. 615 0$aComputer engineering. 615 0$aComputer networks. 615 14$aComputer Vision. 615 24$aArtificial Intelligence. 615 24$aAutomated Pattern Recognition. 615 24$aComputer Engineering and Networks. 676 $a616.07 702 $aFu$b Huazhu 712 12$aOMIA (Workshop) 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910427670103321 996 $aOphthalmic Medical Image Analysis$92568252 997 $aUNINA