001481318 000__ 07165cam\\22006377i\4500
001481318 001__ 1481318
001481318 003__ OCoLC
001481318 005__ 20231031003332.0
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001481318 008__ 231003s2023\\\\sz\a\\\\o\\\\\101\0\eng\d
001481318 020__ $$a9783031439070$$q(electronic bk.)
001481318 020__ $$a3031439074$$q(electronic bk.)
001481318 020__ $$z9783031439063$$q(print)
001481318 0247_ $$a10.1007/978-3-031-43907-0$$2doi
001481318 035__ $$aSP(OCoLC)1401632517
001481318 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP
001481318 049__ $$aISEA
001481318 050_4 $$aRC78.7.D53$$bI58 2023eb
001481318 08204 $$a616.07/54$$223/eng/20231003
001481318 1112_ $$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$$n(26th :$$d2023 :$$cVancouver, B.C. ; Online)
001481318 24510 $$aMedical image computing and computer assisted intervention – MICCAI 2023 :$$b26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings.$$nPart I /$$cHayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor, editors.
001481318 2463_ $$aMICCAI 2023
001481318 264_1 $$aCham :$$bSpringer,$$c2023.
001481318 300__ $$a1 online resource (xxxviii, 785 pages) :$$billustrations (some color).
001481318 336__ $$atext$$btxt$$2rdacontent
001481318 337__ $$acomputer$$bc$$2rdamedia
001481318 338__ $$aonline resource$$bcr$$2rdacarrier
001481318 4901_ $$aLecture Notes in Computer Science,$$x1611-3349 ;$$v14220
001481318 500__ $$aIncludes author index.
001481318 5050_ $$aIntro -- Preface -- Organization -- Contents - Part I -- Machine Learning with Limited Supervision -- PET-Diffusion: Unsupervised PET Enhancement Based on the Latent Diffusion Model -- 1 Introduction -- 2 Method -- 2.1 Image Compression -- 2.2 Latent Diffusion Model -- 2.3 Implementation Details -- 3 Experiments -- 3.1 Dataset -- 3.2 Ablation Analysis -- 3.3 Comparison with State-of-the-Art Methods -- 3.4 Generalization Evaluation -- 4 Conclusion and Limitations -- References -- MedIM: Boost Medical Image Representation via Radiology Report-Guided Masking -- 1 Introduction -- 2 Approach
001481318 5058_ $$a2.1 Image and Text Encoders -- 2.2 Report-Guided Mask Generation -- 2.3 Decoder for Reconstruction -- 2.4 Objective Function -- 2.5 Downstream Transfer Learning -- 3 Experiments and Results -- 3.1 Experimental Details -- 3.2 Comparisons with Different Pre-training Methods -- 3.3 Discussions -- 4 Conclusion -- References -- UOD: Universal One-Shot Detection of Anatomical Landmarks -- 1 Introduction -- 2 Method -- 2.1 Stage I: Contrastive Learning -- 2.2 Stage II: Supervised Learning -- 3 Experiment -- 3.1 Experimental Results -- 4 Conclusion -- References
001481318 5058_ $$aS2ME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-Supervised Polyp Segmentation -- 1 Introduction -- 2 Methodology -- 2.1 Preliminaries -- 2.2 S2ME: Spatial-Spectral Mutual Teaching and Ensemble Learning -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Results and Analysis -- 3.3 Ablation Studies -- 4 Conclusion -- References -- Modularity-Constrained Dynamic Representation Learning for Interpretable Brain Disorder Analysis with Functional MRI -- 1 Introduction -- 2 Materials and Methodology -- 2.1 Subjects and Image Preprocessing -- 2.2 Proposed Method -- 3 Experiment
001481318 5058_ $$a4 Discussion -- 5 Conclusion and Future Work -- References -- Anatomy-Driven Pathology Detection on Chest X-rays -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Model -- 3.2 Inference -- 3.3 Training -- 3.4 Dataset -- 4 Experiments and Results -- 4.1 Experimental Setup and Baselines -- 4.2 Pathology Detection Results -- 5 Discussion and Conclusion -- References -- VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis -- 1 Introduction -- 2 Methods -- 3 Experimental Setup -- 4 Results -- 5 Conclusions -- References
001481318 5058_ $$aDense Transformer based Enhanced Coding Network for Unsupervised Metal Artifact Reduction -- 1 Introduction -- 2 Methodology -- 2.1 Hierarchical Disentangling Encoder (HDE) -- 2.2 Dense Transformer for Disentanglement (DTD) -- 2.3 Second-Order Disentanglement for MA Reduction (SOD-MAR) -- 2.4 Loss Function -- 3 Empirical Results -- 3.1 Ablation Study -- 3.2 Comparison to State-of-the-Art (SOTA) -- 4 Conclusion -- References -- Multi-scale Cross-restoration Framework for Electrocardiogram Anomaly Detection -- 1 Introduction -- 2 Method -- 2.1 Multi-scale Cross-restoration
001481318 506__ $$aAccess limited to authorized users.
001481318 520__ $$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.
001481318 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 3, 2023).
001481318 650_0 $$aDiagnostic imaging$$xData processing$$vCongresses.$$0(DLC)sh2007006024
001481318 655_0 $$aElectronic books.
001481318 7001_ $$aGreenspan, Hayit,$$eeditor.
001481318 7001_ $$aMadabhushi, Anant,$$eeditor.$$1https://orcid.org/0000-0002-5741-0399
001481318 7001_ $$aMousavi, Parvin,$$eeditor.
001481318 7001_ $$aSalcudean, Septimiu Edmund,$$eeditor.$$1https://orcid.org/0000-0001-8826-8025
001481318 7001_ $$aDuncan, James,$$d1951-$$eeditor.$$1https://orcid.org/0000-0002-5167-9856$$0(OCoLC)oca00745149
001481318 7001_ $$aSyeda-Mahmood, Tanveer,$$eeditor.$$1https://orcid.org/0000-0003-0059-3208
001481318 7001_ $$aTaylor, Russell,$$eeditor.$$0(orcid)0000-0001-6272-1100$$1https://orcid.org/0000-0001-6272-1100
001481318 830_0 $$aLecture notes in computer science ;$$v14220.$$x1611-3349
001481318 852__ $$bebk
001481318 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-43907-0$$zOnline Access$$91397441.1
001481318 909CO $$ooai:library.usi.edu:1481318$$pGLOBAL_SET
001481318 980__ $$aBIB
001481318 980__ $$aEBOOK
001481318 982__ $$aEbook
001481318 983__ $$aOnline
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