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001481324 001__ 1481324
001481324 003__ OCoLC
001481324 005__ 20231031003332.0
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001481324 008__ 231003s2023\\\\sz\a\\\\o\\\\\101\0\eng\d
001481324 020__ $$a9783031439964$$q(electronic bk.)
001481324 020__ $$a3031439961$$q(electronic bk.)
001481324 020__ $$z9783031439957$$q(print)
001481324 0247_ $$a10.1007/978-3-031-43996-4$$2doi
001481324 035__ $$aSP(OCoLC)1401635719
001481324 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP
001481324 049__ $$aISEA
001481324 050_4 $$aRC78.7.D53$$bI58 2023eb
001481324 08204 $$a616.07/54$$223/eng/20231003
001481324 1112_ $$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$$n(26th :$$d2023 :$$cVancouver, B.C. ; Online)
001481324 24510 $$aMedical image computing and computer assisted intervention -- MICCAI 2023 :$$b26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings.$$nPart IX /$$cby Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor, editors.
001481324 2463_ $$aMICCAI 2023
001481324 264_1 $$aCham, Switzerland :$$bSpringer,$$c2023.
001481324 300__ $$a1 online resource (xxxviii, 743 pages) :$$billustrations (some color).
001481324 336__ $$atext$$btxt$$2rdacontent
001481324 337__ $$acomputer$$bc$$2rdamedia
001481324 338__ $$aonline resource$$bcr$$2rdacarrier
001481324 4901_ $$aLecture notes in computer science,$$x1611-3349 ;$$v14228
001481324 500__ $$aIncludes author index.
001481324 5050_ $$aIntro -- Preface -- Organization -- Contents - Part IX -- Image-Guided Intervention, Surgical Planning, and Data Science -- UXDiff: Synthesis of X-Ray Image from Ultrasound Coronal Image of Spine with Diffusion Probabilistic Network -- 1 Introduction -- 2 Method -- 2.1 Conditional Diffusion Models for U2X Translation -- 2.2 Attention Module with Angle Embedding -- 2.3 Consistent Conditional Loss -- 3 Experiments -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Synthesis Performance -- 3.4 Comparison with Cobb Angle -- 4 Conclusion -- References
001481324 5058_ $$aEndoSurf: Neural Surface Reconstruction of Deformable Tissues with Stereo Endoscope Videos -- 1 Introduction -- 2 Method -- 2.1 Overview -- 2.2 EndoSurf: Representing Scenes as Deformable Neural Fields -- 2.3 Optimization -- 3 Experiments -- 3.1 Experiment Settings -- 3.2 Qualitative and Quantitative Results -- 4 Conclusion -- References -- Surgical Video Captioning with Mutual-Modal Concept Alignment -- 1 Introduction -- 2 Surgical Concept Alignment Network -- 2.1 Overview of SCA-Net -- 2.2 Surgical Concept Learning -- 2.3 Mutual-Modality Concept Alignment -- 2.4 Optimization -- 3 Experiment
001481324 5058_ $$a3.1 Dataset and Implementation Details -- 3.2 Comparison on Neurosurgery Video Captioning -- 3.3 Comparison on EndoVis Image Captioning -- 4 Conclusion -- References -- SEDSkill: Surgical Events Driven Method for Skill Assessment from Thoracoscopic Surgical Videos -- 1 Introduction -- 2 Method -- 2.1 Basic Regression Module -- 2.2 Local-Global Difference Module -- 3 Experiments -- 3.1 Comparison with the State-of-the-Art Methods -- 3.2 Ablation Study -- 4 Conclusion -- References -- Neural LerPlane Representations for Fast 4D Reconstruction of Deformable Tissues -- 1 Introduction -- 2 Method
001481324 5058_ $$a2.1 Overview -- 2.2 Preliminaries -- 2.3 Neural LerPlane Representations for Deformable Tissues -- 2.4 Spatiotemporal Importance Sampling -- 2.5 Coordinate-Time Encoding -- 2.6 Optimization -- 3 Experiments -- 3.1 Dataset and Evaluation Metrics -- 3.2 Implementation Details -- 3.3 Evaluation -- 3.4 Ablation Study -- 4 Conclusion and Future Work -- References -- SegmentOR: Obtaining Efficient Operating Room Semantics Through Temporal Propagation -- 1 Introduction -- 2 Related Work -- 3 Method -- 4 Experiments -- 5 Results and Discussion -- References
001481324 5058_ $$aRevisiting Distillation for Continual Learning on Visual Question Localized-Answering in Robotic Surgery -- 1 Introduction -- 2 Methodology -- 2.1 Preliminaries -- 2.2 Continual Surgical VQLA (CS-VQLA) -- 3 Experiments -- 3.1 Dataset and Setup -- 3.2 Results -- 4 Conclusion -- References -- Intra-operative Forecasting of Standing Spine Shape with Articulated Neural Kernel Fields -- 1 Introduction -- 2 Methods -- 2.1 Intra-operative 3D Model from Multi-view X-Rays -- 2.2 Learnable Shape Deformation Kernel -- 2.3 Articulation SDF -- 3 Experiments -- 4 Conclusion -- References
001481324 506__ $$aAccess limited to authorized users.
001481324 520__ $$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.
001481324 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 3, 2023).
001481324 650_0 $$aDiagnostic imaging$$xData processing$$vCongresses.$$0(DLC)sh2007006024
001481324 655_0 $$aElectronic books.
001481324 7001_ $$aGreenspan, Hayit,$$eeditor.
001481324 7001_ $$aMadabhushi, Anant,$$eeditor.$$1https://orcid.org/0000-0002-5741-0399
001481324 7001_ $$aMousavi, Parvin,$$eeditor.
001481324 7001_ $$aSalcudean, Septimiu Edmund,$$eeditor.$$1https://orcid.org/0000-0001-8826-8025
001481324 7001_ $$aDuncan, James,$$d1951-$$eeditor.$$1https://orcid.org/0000-0002-5167-9856$$0(OCoLC)oca00745149
001481324 7001_ $$aSyeda-Mahmood, Tanveer,$$eeditor.$$1https://orcid.org/0000-0003-0059-3208
001481324 7001_ $$aTaylor, Russell,$$eeditor.$$0(orcid)0000-0001-6272-1100$$1https://orcid.org/0000-0001-6272-1100
001481324 830_0 $$aLecture notes in computer science ;$$v14228.$$x1611-3349
001481324 852__ $$bebk
001481324 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-43996-4$$zOnline Access$$91397441.1
001481324 909CO $$ooai:library.usi.edu:1481324$$pGLOBAL_SET
001481324 980__ $$aBIB
001481324 980__ $$aEBOOK
001481324 982__ $$aEbook
001481324 983__ $$aOnline
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