LEADER 01050nam a2200265|i 4500 001 991003797329707536 005 20021223130746.0 008 020226s|||| it u u enguu 020 $a8814090467 035 $ab11865453-39ule_inst 035 $aLE02988636$9ExL 040 $aISUFI - Sett. Diritti e Politiche Euromediterranee$bita 041 0 $aengita 082 0 $a346.048 100 1 $aGhidini, Gustavo$0120268 245 10$aProfili evolutivi del diritto industriale :$bproprieta intellettuale e concorrenza /$cGustavo Ghidini ; prefazione di Jerome H. Reichman 260 0 $aMilano :$bGiuffre,$cc2001 300 $axviii, 274 p. ;$c24 cm 650 4$aProprietà intellettuale 907 $a.b11865453$b02-04-14$c23-12-02 912 $a991003797329707536 945 $aLE029 346.048 GHI01.01$g1$iLE029-3366$lle029$nDir. Pol. Euromediterranee$o-$pE0.00$q-$rn$so $t0$u0$v0$w0$x0$y.i12118059$z23-12-02 996 $aProfili evolutivi del diritto industriale$966513 997 $aUNISALENTO 998 $ale029$b01-01-02$cm$da $e-$feng$git $h0$i1 LEADER 05127nam 22007095 450 001 9910349273003321 005 20251107172042.0 010 $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(MiAaPQ)EBC6275359 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 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v11855 311 08$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,$x3004-9954 ;$v11855 606 $aComputer vision 606 $aArtificial intelligence 606 $aComputer science$xMathematics 606 $aComputer engineering 606 $aComputer networks 606 $aComputer Vision 606 $aArtificial Intelligence 606 $aMathematics of Computing 606 $aComputer Engineering and Networks 615 0$aComputer vision. 615 0$aArtificial intelligence. 615 0$aComputer science$xMathematics. 615 0$aComputer engineering. 615 0$aComputer networks. 615 14$aComputer Vision. 615 24$aArtificial Intelligence. 615 24$aMathematics of Computing. 615 24$aComputer Engineering and 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