LEADER 00674nam0-22002531i-450 001 990002593860403321 005 20230426102733.0 035 $a000259386 035 $aFED01000259386 035 $a(Aleph)000259386FED01 035 $a000259386 100 $a20000920d1965----km-y0itay50------ba 101 0 $aeng 102 $aUS 200 1 $aAccounting and the law$fdi SIMON 210 $aNew Brunswick$cBureau of Business Research$d1965 700 1$aSimon,$bS.I.$0368713 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990002593860403321 952 $aC2-P12-22-RA$bs.i.$fECA 959 $aECA 996 $aAccounting and the law$9435580 997 $aUNINA LEADER 08450nam 22009015 450 001 9910349405203321 005 20251225203644.0 010 $a9783030009496 010 $a3030009491 024 7 $a10.1007/978-3-030-00949-6 035 $a(CKB)4100000006674679 035 $a(DE-He213)978-3-030-00949-6 035 $a(MiAaPQ)EBC6281845 035 $a(PPN)230538924 035 $a(EXLCZ)994100000006674679 100 $a20180913d2018 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputational Pathology and Ophthalmic Medical Image Analysis $eFirst International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16 - 20, 2018, Proceedings /$fedited by Danail Stoyanov, Zeike Taylor, Francesco Ciompi, Yanwu Xu, Anne Martel, Lena Maier-Hein, Nasir Rajpoot, Jeroen van der Laak, Mitko Veta, Stephen McKenna, David Snead, Emanuele Trucco, Mona K. Garvin, Xin Jan Chen, Hrvoje Bogunovic 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (XVII, 347 p. 135 illus.) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v11039 300 $aIncludes index. 311 08$a9783030009489 311 08$a3030009483 327 $aImproving Accuracy of Nuclei Segmentation by Reducing Histological Image Variability -- Multi-Resolution Networks for Semantic Segmentation in Whole Slide Images -- Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network -- Construction of a Generative Model of H&E Stained Pathology Images of Pancreas Tumors Conditioned by a Voxel Value of MRI Image -- Accurate 3D reconstruction of a whole pancreatic cancer tumor from pathology images with different stains -- Role of Task Complexity and Training in Crowdsourced Image Annotation -- Capturing global spatial context for accurate cell classification in skin cancer histology -- Exploiting Multiple Color Representations to Improve Colon Cancer Detection in Whole Slide H&E Stains -- Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection -- Evaluating Out-of-the-box Methods for the Classification of Hematopoietic Cells in Images of Stained Bone Marrow -- DeepCerv: Deep neural network for segmentation free robustcervical cell classification -- Whole slide image registration for the study of tumor heterogeneity -- Modality Conversion from Pathological Image to Ultrasonic Image Using Convolutional Neural Network -- Structure instance segmentation in renal tissue: a case study on tubular immune cell detection -- Cellular Community Detection for Tissue Phenotyping in Histology Images -- Automatic Detection of Tumor Budding in Colorectal Carcinoma with Deep Learning -- Significance of Hyperparameter Optimization for Metastasis Detection in Breast Histology Images -- Image Magnification Regression Using DenseNet for Exploiting Histopathology Open Access Content -- Uncertainty Driven Pooling Network for Microvessel Segmentation in Routine Histology Images -- Ocular Structures Segmentation from Multi-sequences MRI using 3D Unet with Fully Connected CRFs -- Classification of Findings with Localized Lesions in Fundoscopic Images using a Regionally Guided CNN -- Segmentation of Corneal Nerves Using a U-Net-based Convolutional Neural Network -- Automatic Pigmentation Grading of the Trabecular Meshwork in Gonioscopic Images -- Large Receptive Field Fully Convolutional Network for Semantic Segmentation of Retinal Vasculature in Fundus Images -- Explaining Convolutional Neural Networks for Area Estimation of Choroidal Neovascularization via Genetic Programming -- Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images using Bayesian Deep Learning -- cGAN-based lacquer cracks segmentation in ICGA image -- Localizing Optic Disc and Cup for Glaucoma Screening via Deep Object Detection Networks -- Fundus Image Quality-guided Diabetic Retinopathy Grading -- DeepDisc: Optic Disc Segmentation based on Atrous Convolution and Spatial Pyramid Pooling -- Large-scale Left and Right Eye Classification in Retinal Images -- Automatic Segmentation of Cortex and Nucleus in Anterior Segment OCT Images -- Local Estimation of the Degree of Optic Disc Swelling from Color Fundus Photography -- Visual Field based Automatic Diagnosis of Glaucoma Using Deep Convolutional Neural Network -- Towards standardization of retinal vascular measurements: on the effect of image centering -- Feasibility study of Subfoveal Choroidal Thickness Changes in Spectral-Domain Optical Coherence Tomography Measurements of Macular Telangiectasia Type 2 -- Segmentation of retinal layers in OCT images of the mouse eye utilizing polarization contrast -- Glaucoma Diagnosis from Eye Fundus Images Based on Deep Morphometric Feature Estimation -- 2D Modeling and Correction of Fan-beam Scan Geometry in OCT -- A Bottom-up Saliency Estimation Approach for Neonatal Retinal Images. 330 $aThis book constitutes the refereed joint proceedings of the First International Workshop on Computational Pathology, COMPAY 2018, and the 5th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 19 full papers (out of 25 submissions) presented at COMPAY 2018 and the 21 full papers (out of 31 submissions) presented at OMIA 2018 were carefully reviewed and selected. The COMPAY papers focus on artificial intelligence and deep learning. The OMIA papers cover various topics in the field of ophthalmic image analysis. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v11039 606 $aComputer vision 606 $aArtificial intelligence 606 $aComputer arithmetic and logic units 606 $aComputer science$xMathematics 606 $aMathematical statistics 606 $aPattern recognition systems 606 $aComputer Vision 606 $aArtificial Intelligence 606 $aArithmetic and Logic Structures 606 $aProbability and Statistics in Computer Science 606 $aAutomated Pattern Recognition 615 0$aComputer vision. 615 0$aArtificial intelligence. 615 0$aComputer arithmetic and logic units. 615 0$aComputer science$xMathematics. 615 0$aMathematical statistics. 615 0$aPattern recognition systems. 615 14$aComputer Vision. 615 24$aArtificial Intelligence. 615 24$aArithmetic and Logic Structures. 615 24$aProbability and Statistics in Computer Science. 615 24$aAutomated Pattern Recognition. 676 $a617.7 676 $a616.07 702 $aStoyanov$b Danail$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aTaylor$b Zeike$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aCiompi$b Francesco$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aXu$b Yanwu$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMartel$b Anne$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMaier-Hein$b Lena$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRajpoot$b Nasir$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $avan der Laak$b Jeroen$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aVeta$b Mitko$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMcKenna$b Stephen$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSnead$b David$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aTrucco$b Emanuele$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGarvin$b Mona K.$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aChen$b Xin Jan$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBogunovic$b Hrvoje$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910349405203321 996 $aComputational Pathology and Ophthalmic Medical Image Analysis$91912431 997 $aUNINA