LEADER 01468nam 2200325Ia 450 001 996383500303316 005 20200824132146.0 035 $a(CKB)1000000000589043 035 $a(EEBO)2240886601 035 $a(OCoLC)ocm16961069e 035 $a(OCoLC)16961069 035 $a(EXLCZ)991000000000589043 100 $a19871110d1642 uy | 101 0 $aeng 135 $aurbn#|||a|bb| 200 10$aTwo letters, the one from the Lord Digby to the Queens Majestie, the other from Mr. Thomas Elliot to the Lord Digby, with observations upon the same letters$b[electronic resource] $ealso a note of such armes as were sent for by His Majestie out of Amsterdam, under his owne hand : likewise the opposition the Marquesse of Hartford received, in executing His Majesties illegal commission of array in Sommerset-shire 210 $aLondon $cPrinted for George Lindsey$d1642 215 $a[2], 5, [1] p 300 $aReproduction of original in the Bodleian Library. 330 $aeebo-0014 607 $aGreat Britain$xHistory$yCivil War, 1642-1649 700 $aBristol$b George Digby$cEarl of,$f1612-1677.$01001102 701 $aElliot$b Thomas$01008029 801 0$bEAK 801 1$bEAK 801 2$bWaOLN 906 $aBOOK 912 $a996383500303316 996 $aTwo letters, the one from the Lord Digby to the Queens Majestie, the other from Mr. Thomas Elliot to the Lord Digby, with observations upon the same letters$92323947 997 $aUNISA LEADER 04774oam 2200529 450 001 996418214703316 005 20231211224339.0 010 $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 $a20210505d2021 uy 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aOphthalmic medical image analysis $e7th International Workshop, OMIA 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, proceedings /$fHuazhu Fu [and four others] editors 205 $a1st ed. 2020. 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$dİ2021 215 $a1 online resource (IX, 218 p.) $c19 illus 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$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 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. 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 ;$v12069 517 3 $aOMIA 2020 606 $aPathology$xData processing$vCongresses 606 $aEye$xImaging$vCongresses 615 0$aPathology$xData processing 615 0$aEye$xImaging 676 $a616.07 702 $aFu$b Huazhu 712 12$aOMIA (Workshop) 801 0$bCaPaEBR 801 1$bCaPaEBR 801 2$bUtOrBLW 906 $aBOOK 912 $a996418214703316 996 $aOphthalmic Medical Image Analysis$92568252 997 $aUNISA