LEADER 02441nam 22005774a 450 001 9910451607903321 005 20200520144314.0 010 $a1-282-53503-X 010 $a9786612535031 010 $a0-313-08622-2 035 $a(CKB)1000000000487739 035 $a(EBL)497547 035 $a(OCoLC)143283098 035 $a(SSID)ssj0000251904 035 $a(PQKBManifestationID)11206776 035 $a(PQKBTitleCode)TC0000251904 035 $a(PQKBWorkID)10175101 035 $a(PQKB)11182524 035 $a(MiAaPQ)EBC497547 035 $a(Au-PeEL)EBL497547 035 $a(CaPaEBR)ebr10373234 035 $a(CaONFJC)MIL253503 035 $a(EXLCZ)991000000000487739 100 $a20060223d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 14$aThe strange career of the Black athlete$b[electronic resource] $eAfrican Americans and sports /$fRussell T. Wigginton ; forward [sic] by Benjamin L. Hooks 210 $aWestport, Conn. $cPraeger$d2006 215 $a1 online resource (142 p.) 300 $aDescription based upon print version of record. 311 $a0-275-98223-8 320 $aIncludes bibliographical references (p. [115]-116) and index. 327 $aCan they really play? African American participation in "white" sports -- Heroes or villains? the categorization of African-American star athletes, 1892-1946 -- When the rooster crows : African American athletes in the struggle for civil rights, 1954-1968 -- The rules have changed but the game is still the same : black athletes' achievements in the civil rights era and beyond -- She's done more for her country than what the U.S. could have paid her for : African American women and sports. 330 $aShows how sports have at times challenged racial mores yet at other times reinforced the status quo in this revealing historical group portrait of black athletes. 606 $aAfrican American athletes$xHistory 606 $aSports$zUnited States$xHistory 608 $aElectronic books. 615 0$aAfrican American athletes$xHistory. 615 0$aSports$xHistory. 676 $a796.089/96073 700 $aWigginton$b Russell Thomas$0902722 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910451607903321 996 $aThe strange career of the Black athlete$92017995 997 $aUNINA LEADER 08684nam 22008415 450 001 996466199903316 005 20200902195123.0 010 $a3-030-00949-1 024 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$b[electronic resource] $eFirst International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16 - 20, 2018, Proceedings /$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 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v11039 300 $aIncludes index. 311 $a3-030-00948-3 327 $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 robust cervical 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 ;$v11039 606 $aOptical data processing 606 $aArtificial intelligence 606 $aArithmetic and logic units, Computer 606 $aMathematical statistics 606 $aPattern recognition 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 $aArithmetic and Logic Structures$3https://scigraph.springernature.com/ontologies/product-market-codes/I12026 606 $aProbability and Statistics in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17036 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 615 0$aOptical data processing. 615 0$aArtificial intelligence. 615 0$aArithmetic and logic units, Computer. 615 0$aMathematical statistics. 615 0$aPattern recognition. 615 14$aImage Processing and Computer Vision. 615 24$aArtificial Intelligence. 615 24$aArithmetic and Logic Structures. 615 24$aProbability and Statistics in Computer Science. 615 24$aPattern Recognition. 676 $a617.7 702 $aStoyanov$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 $a996466199903316 996 $aComputational Pathology and Ophthalmic Medical Image Analysis$91912431 997 $aUNISA