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001481319 001__ 1481319
001481319 003__ OCoLC
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001481319 020__ $$a9783031439018$$q(electronic bk.)
001481319 020__ $$a3031439015$$q(electronic bk.)
001481319 020__ $$z9783031439001$$q(print)
001481319 0247_ $$a10.1007/978-3-031-43901-8$$2doi
001481319 035__ $$aSP(OCoLC)1401632612
001481319 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP
001481319 049__ $$aISEA
001481319 050_4 $$aRC78.7.D53$$bI58 2023eb
001481319 08204 $$a616.07/54$$223/eng/20231003
001481319 1112_ $$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$$n(26th :$$d2023 :$$cVancouver, B.C. ; Online)
001481319 24510 $$aMedical image computing and computer assisted intervention -- MICCAI 2023 :$$b26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings.$$nPart IV /$$cHayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor, editors.
001481319 2463_ $$aMICCAI 2023
001481319 264_1 $$aCham, Switzerland :$$bSpringer,$$c2023.
001481319 300__ $$a1 online resource (xxxviii, 801 pages) :$$billustrations (some color).
001481319 336__ $$atext$$btxt$$2rdacontent
001481319 337__ $$acomputer$$bc$$2rdamedia
001481319 338__ $$aonline resource$$bcr$$2rdacarrier
001481319 4901_ $$aLecture notes in computer science,$$x1611-3349 ;$$v14223
001481319 500__ $$aIncludes author index.
001481319 5050_ $$aIntro -- Preface -- Organization -- Contents - Part IV -- Image Segmentation II -- Category-Level Regularized Unlabeled-to-Labeled Learning for Semi-supervised Prostate Segmentation with Multi-site Unlabeled Data -- 1 Introduction -- 2 Methods -- 2.1 Problem Formulation and Basic Architecture -- 2.2 Pseudo Labeling for Local Distribution Fitting -- 2.3 Category-Level Regularized Unlabeled-to-Labeled Learning -- 3 Experiments and Results -- 4 Conclusion -- References -- Devil is in Channels: Contrastive Single Domain Generalization for Medical Image Segmentation -- 1 Introduction -- 2 Method
001481319 5058_ $$a2.1 Problem Definition and Method Overview -- 2.2 Style Augmentation -- 2.3 Contrastive Feature Disentanglement -- 2.4 Training and Inference -- 3 Experiments and Results -- 4 Conclusion -- References -- Transformer-Based Annotation Bias-Aware Medical Image Segmentation -- 1 Introduction -- 2 Method -- 2.1 Problem Formalization and Method Overview -- 2.2 CNN Encoder -- 2.3 PFE Module -- 2.4 SS Head -- 2.5 Loss and Inference -- 3 Experiments and Results -- 3.1 Dataset and Experimental Setup -- 3.2 Comparative Experiments -- 3.3 Ablation Analysis -- 4 Conclusion -- References
001481319 5058_ $$aUncertainty-Informed Mutual Learning for Joint Medical Image Classification and Segmentation -- 1 Introduction -- 2 Method -- 2.1 Uncertainty Estimation for Classification and Segmentation -- 2.2 Uncertainty-Informed Mutual Learning -- 2.3 Mutual Learning Process -- 3 Experiments -- 4 Conclusion -- References -- A General Stitching Solution for Whole-Brain 3D Nuclei Instance Segmentation from Microscopy Images -- 1 Introduction -- 2 Methods -- 2.1 Graph Contextual Model -- 2.2 Hierarchical Stitching Framework for Whole-Brain NIS -- 2.3 Implementation Details -- 3 Experiments
001481319 5058_ $$a3.1 Experimental Settings -- 3.2 Evaluation Metrics -- 3.3 Evaluating the Accuracy of NIS Stitching Results -- 3.4 Whole-Brain NIS in Neuroscience Applications -- 4 Conclusion -- References -- Adult-Like Phase and Multi-scale Assistance for Isointense Infant Brain Tissue Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Semantics-Preserved Multi-phase Synthesis -- 2.2 Transformer-Based Multi-scale Segmentation -- 3 Experiments and Results -- 3.1 Dataset and Evaluation Metrics -- 3.2 Implementation Details -- 3.3 Evaluation and Discussion -- 4 Conclusion -- References
001481319 5058_ $$aRobust Segmentation via Topology Violation Detection and Feature Synthesis -- 1 Introduction -- 2 Method -- 3 Evaluation -- 4 Conclusion -- References -- GL-Fusion: Global-Local Fusion Network for Multi-view Echocardiogram Video Segmentation -- 1 Introduction -- 2 Methodology -- 2.1 The Overall Framework -- 2.2 Multi-view Global-Local Fusion Module -- 2.3 Dense Cycle Loss -- 3 Experiment -- 3.1 Comparison with the State-of-the-Art Methods -- 3.2 Ablation Study -- 4 Conclusion -- References
001481319 506__ $$aAccess limited to authorized users.
001481319 520__ $$aThe ten-volume set LNCS 14220, 14221, 14222, 14223, 14224, 14225, 14226, 14227, 14228, and 14229 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023. The 730 revised full papers presented were carefully reviewed and selected from a total of 2250 submissions. The papers are organized in the following topical sections: Part I: Machine learning with limited supervision and machine learning - transfer learning; Part II: Machine learning -- learning strategies; machine learning -- explainability, bias, and uncertainty; Part III: Machine learning -- explainability, bias and uncertainty; image segmentation; Part IV: Image segmentation; Part V: Computer-aided diagnosis; Part VI: Computer-aided diagnosis; computational pathology; Part VII: Clinical applications -- abdomen; clinical applications -- breast; clinical applications -- cardiac; clinical applications -- dermatology; clinical applications -- fetal imaging; clinical applications -- lung; clinical applications -- musculoskeletal; clinical applications -- oncology; clinical applications -- ophthalmology; clinical applications -- vascular; Part VIII: Clinical applications -- neuroimaging; microscopy; Part IX: Image-guided intervention, surgical planning, and data science; Part X: Image reconstruction and image registration.
001481319 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 3, 2023).
001481319 650_0 $$aDiagnostic imaging$$xData processing$$vCongresses.$$0(DLC)sh2007006024
001481319 655_0 $$aElectronic books.
001481319 7001_ $$aGreenspan, Hayit,$$eeditor.
001481319 7001_ $$aMadabhushi, Anant,$$eeditor.$$1https://orcid.org/0000-0002-5741-0399
001481319 7001_ $$aMousavi, Parvin,$$eeditor.
001481319 7001_ $$aSalcudean, Septimiu Edmund,$$eeditor.$$1https://orcid.org/0000-0001-8826-8025
001481319 7001_ $$aDuncan, James,$$d1951-$$eeditor.$$1https://orcid.org/0000-0002-5167-9856$$0(OCoLC)oca00745149
001481319 7001_ $$aSyeda-Mahmood, Tanveer,$$eeditor.$$1https://orcid.org/0000-0003-0059-3208
001481319 7001_ $$aTaylor, Russell,$$eeditor.$$0(orcid)0000-0001-6272-1100$$1https://orcid.org/0000-0001-6272-1100
001481319 830_0 $$aLecture notes in computer science ;$$v14223.$$x1611-3349
001481319 852__ $$bebk
001481319 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-43901-8$$zOnline Access$$91397441.1
001481319 909CO $$ooai:library.usi.edu:1481319$$pGLOBAL_SET
001481319 980__ $$aBIB
001481319 980__ $$aEBOOK
001481319 982__ $$aEbook
001481319 983__ $$aOnline
001481319 994__ $$a92$$bISE