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001445446 001__ 1445446
001445446 003__ OCoLC
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001445446 019__ $$a1306052290
001445446 020__ $$a9783030983857$$q(electronic bk.)
001445446 020__ $$a3030983854$$q(electronic bk.)
001445446 020__ $$z9783030983840
001445446 020__ $$z3030983846
001445446 0247_ $$a10.1007/978-3-030-98385-7$$2doi
001445446 035__ $$aSP(OCoLC)1306024300
001445446 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dGW5XE$$dEBLCP$$dOCLCO$$dOCLCF$$dUKAHL$$dOCLCQ
001445446 049__ $$aISEA
001445446 050_4 $$aRC280.K5$$bI58 2021
001445446 08204 $$a616.99/461$$223
001445446 1112_ $$aInternational Challenge on Kidney and Kidney Tumor Segmentation$$n(2nd :$$d2021 :$$cOnline).
001445446 24510 $$aKidney and kidney tumor segmentation :$$bMICCAI 2021 Challenge, KiTS 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, proceedings /$$cNicholas Heller, Fabian Isensee, Darya Trofimova, Resha Tejpaul, Nikolaos Papanikolopoulos, Christopher Weight (eds.).
001445446 24630 $$aMICCAI 2021 Challenge, KiTS 2021
001445446 264_1 $$aCham :$$bSpringer,$$c[2022]
001445446 264_4 $$c©2022
001445446 300__ $$a1 online resource :$$billustrations (chiefly color).
001445446 336__ $$atext$$btxt$$2rdacontent
001445446 337__ $$acomputer$$bc$$2rdamedia
001445446 338__ $$aonline resource$$bcr$$2rdacarrier
001445446 4901_ $$aLecture notes in computer science,$$x1611-3349 ;$$v13168
001445446 500__ $$aSelected conference papers.
001445446 500__ $$aIncludes author index.
001445446 5050_ $$aAutomated kidney tumor segmentation with convolution and transformer network -- Extraction of Kidney Anatomy based on a 3D U-ResNet with Overlap-Tile Strategy -- Modified nnU-Net for the MICCAI KiTS21 Challenge -- 2.5D Cascaded Semantic Segmentation for Kidney Tumor Cyst -- Automated Machine Learning algorithm for Kidney, Kidney tumor, Kidney Cyst segmentation in Computed Tomography Scans -- Three Uses of One Neural Network: Automatic Segmentation of Kidney Tumor and Cysts Based on 3D U-Net -- Less is More: Contrast Attention assisted U-Net for Kidney, Tumor and Cyst Segmentations -- A Coarse-to-fine Framework for The 2021 Kidney and Kidney Tumor Segmentation Challenge -- Kidney and kidney tumor segmentation using a two-stage cascade framework -- Squeeze-and-Excitation Encoder-Decoder Network for Kidney and Kidney Tumor Segmentation in CT images -- A Two-stage Cascaded Deep Neural Network with Multi-decoding Paths for Kidney Tumor Segmentation -- Mixup Augmentation for Kidney and Kidney Tumor Segmentation -- Automatic Segmentation in Abdominal CT Imaging for the KiTS21 Challenge -- An Ensemble of 3D U-Net Based Models for Segmentation of Kidney and Masses in CT Scans -- Contrast-Enhanced CT Renal Tumor Segmentation -- A Cascaded 3D Segmentation Model for Renal Enhanced CT Images -- Leveraging Clinical Characteristics for Improved Deep Learning-Based Kidney Tumor Segmentation on CT -- A Coarse-to-Fine 3D U-Net Network for Semantic Segmentation of Kidney CT Scans -- 3D U-Net Based Semantic Segmentation of Kidneys and Renal Masses on Contrast-Enhanced CT -- Kidney and Kidney Tumor Segmentation using Spatial and Channel attention enhanced U-Net Transfer Learning for KiTS21 Challenge.
001445446 506__ $$aAccess limited to authorized users.
001445446 520__ $$aThis book constitutes the Second International Challenge on Kidney and Kidney Tumor Segmentation, KiTS 2021, which was held in conjunction with the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021. The challenge took place virtually on September 27, 2021, due to the COVID-19 pandemic. The 21 contributions presented were carefully reviewed and selected from 29 submissions. This challenge aims to develop the best system for automatic semantic segmentation of renal tumors and surrounding anatomy.
001445446 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed April 5, 2022).
001445446 650_0 $$aKidneys$$xTumors$$xImaging$$vCongresses.
001445446 650_6 $$aReins$$xTumeurs$$xImagerie$$vCongrès.
001445446 655_0 $$aElectronic books.
001445446 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001445446 655_7 $$aConference papers and proceedings.$$2lcgft
001445446 655_7 $$aActes de congrès.$$2rvmgf
001445446 7001_ $$aHeller, Nicholas$$c(Doctoral student),$$eeditor.
001445446 7001_ $$aIsensee, Fabian,$$eeditor.
001445446 7001_ $$aTrofimova, Darya,$$eeditor.
001445446 7001_ $$aTejpaul, Resha,$$eeditor.
001445446 7001_ $$aPapanikolopoulos, Nikolaos,$$eeditor.
001445446 7001_ $$aWeight, Christopher,$$eeditor.
001445446 7112_ $$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$$n(24th :$$d2021 :$$cOnline)
001445446 77608 $$iPrint version: $$z3030983846$$z9783030983840$$w(OCoLC)1296416557
001445446 830_0 $$aLecture notes in computer science ;$$v13168.$$x1611-3349
001445446 852__ $$bebk
001445446 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-98385-7$$zOnline Access$$91397441.1
001445446 909CO $$ooai:library.usi.edu:1445446$$pGLOBAL_SET
001445446 980__ $$aBIB
001445446 980__ $$aEBOOK
001445446 982__ $$aEbook
001445446 983__ $$aOnline
001445446 994__ $$a92$$bISE