LEADER 05411nam 22006735 450 001 9910349273003321 005 20200706054308.0 010 $a3-030-32956-9 024 7 $a10.1007/978-3-030-32956-3 035 $a(CKB)4100000009522960 035 $a(DE-He213)978-3-030-32956-3 035 $a(MiAaPQ)EBC6283256 035 $a(PPN)255933533 035 $a(EXLCZ)994100000009522960 100 $a20191008d2019 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aOphthalmic Medical Image Analysis $e6th International Workshop, OMIA 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, Proceedings /$fedited by Huazhu Fu, Mona K. Garvin, Tom MacGillivray, Yanwu Xu, Yalin Zheng 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (XI, 192 p. 80 illus., 78 illus. in color.) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v11855 311 $a3-030-32955-0 327 $aDictionary Learning Informed Deep Neural Network with Application to OCT Images -- Structure-aware Noise Reduction Generative Adversarial Network for Optical Coherence Tomography Image -- Region-Based Segmentation of Capillary Density in Optical Coherence Tomography Angiography -- An ampli?ed-target loss approach for photoreceptor layer segmentation in pathological OCT scans -- Foveal avascular zone segmentation in clinical routine ?uorescein angiographies using multitask learning -- Guided M-Net for High-resolution Biomedical Image Segmentation with Weak Boundaries -- 3D-CNN for Glaucoma Detection using Optical Coherence Tomography -- Semi-supervised Adversarial Learning for Diabetic Retinopathy Screening -- Shape Decomposition of Foveal Pit Morphology using Scan Geometry Corrected OCT -- U-Net with spatial pyramid pooling for drusen segmentation in optical coherence tomography -- Deriving Visual Cues from Deep Learning to Achieve Subpixel Cell Segmentation in Adaptive Optics Retinal Images -- Robust Optic Disc Localization by Large Scale Learning -- The Channel Attention based Context Encoder Network for Inner Limiting Membrane Detections -- Fundus Image based Retinal Vessel Segmentation Utilizing A Fast and Accurate Fully Convolutional Network -- Network pruning for OCT image classi?cation -- An improved MPB-CNN segmentation method for edema area and neurosensory retinal detachment in SD-OCT images -- Encoder-Decoder Attention Network for Lesion Segmentation of Diabetic Retinopathy -- Multi-Discriminator Generative Adversarial Networks for improved thin retinal vessel segmentation -- Fovea Localization in Fundus Photographs by Faster R-CNN with Physiological Prior -- Aggressive Posterior Retinopathy of Prematurity Automated Diagnosis via a Deep Convolutional Network -- Automated Stage Analysis of Retinopathy of Prematurity Using Joint Segmentation and Multi-Instance Learning -- Retinopathy Diagnosis using Semi-supervised Multi-channel Generative Adversarial Network. 330 $aThis book constitutes the refereed proceedings of the 6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 22 full papers (out of 36 submissions) presented at OMIA 2019 were carefully reviewed and selected. The papers cover various topics in the field of ophthalmic image analysis. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v11855 606 $aOptical data processing 606 $aArtificial intelligence 606 $aComputer science?Mathematics 606 $aComputer organization 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aMathematics of Computing$3https://scigraph.springernature.com/ontologies/product-market-codes/I17001 606 $aComputer Systems Organization and Communication Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/I13006 615 0$aOptical data processing. 615 0$aArtificial intelligence. 615 0$aComputer science?Mathematics. 615 0$aComputer organization. 615 14$aImage Processing and Computer Vision. 615 24$aArtificial Intelligence. 615 24$aMathematics of Computing. 615 24$aComputer Systems Organization and Communication Networks. 676 $a617.7 676 $a616.07 702 $aFu$b Huazhu$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGarvin$b Mona K$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMacGillivray$b Tom$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aXu$b Yanwu$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aZheng$b Yalin$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910349273003321 996 $aOphthalmic Medical Image Analysis$92568252 997 $aUNINA