001481317 000__ 07189cam\\22006377i\4500
001481317 001__ 1481317
001481317 003__ OCoLC
001481317 005__ 20231031003332.0
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001481317 008__ 231003s2023\\\\sz\a\\\\o\\\\\101\0\eng\d
001481317 020__ $$a9783031439995$$q(electronic bk.)
001481317 020__ $$a3031439996$$q(electronic bk.)
001481317 020__ $$z9783031439988
001481317 0247_ $$a10.1007/978-3-031-43999-5$$2doi
001481317 035__ $$aSP(OCoLC)1401632474
001481317 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP
001481317 049__ $$aISEA
001481317 050_4 $$aRC78.7.D53$$bI58 2023eb
001481317 08204 $$a616.07/54$$223/eng/20231003
001481317 1112_ $$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$$n(26th :$$d2023 :$$cVancouver, B.C. ; Online)
001481317 24510 $$aMedical image computing and computer assisted intervention -- MICCAI 2023 :$$b26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings.$$nPart X /$$cHayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor, editors.
001481317 2463_ $$aMICCAI 2023
001481317 264_1 $$aCham :$$bSpringer,$$c2023.
001481317 300__ $$a1 online resource (xxxviii, 795 pages) :$$billustrations (some color).
001481317 336__ $$atext$$btxt$$2rdacontent
001481317 337__ $$acomputer$$bc$$2rdamedia
001481317 338__ $$aonline resource$$bcr$$2rdacarrier
001481317 4901_ $$aLecture notes in computer science,$$x1611-3349 ;$$v14229
001481317 500__ $$aIncludes author index.
001481317 5050_ $$aIntro -- Preface -- Organization -- Contents - Part X -- Image Reconstruction -- CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI? -- 1 Introduction -- 2 Methodology -- 2.1 Model Components and Training -- 2.2 K-Space Conditioning Reverse Process -- 3 Experimental Results -- 3.1 Implementation Details and Evaluation Methods -- 3.2 Comparison and Ablation Studies -- 4 Discussion and Conclusion -- References -- Learning Deep Intensity Field for Extremely Sparse-View CBCT Reconstruction -- 1 Introduction -- 2 Method -- 2.1 Intensity Field
001481317 5058_ $$a2.2 DIF-Net: Deep Intensity Field Network -- 2.3 Network Training -- 2.4 Volume Reconstruction -- 3 Experiments -- 3.1 Experimental Settings -- 3.2 Results -- 4 Conclusion -- References -- Revealing Anatomical Structures in PET to Generate CT for Attenuation Correction -- 1 Introduction -- 2 Method -- 3 Experiments -- 3.1 Materials -- 3.2 Comparison with Other Methods -- 4 Conclusion -- References -- LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion -- 1 Introduction -- 2 Methodology -- 2.1 Preliminaries -- 2.2 Proposed Methodology
001481317 5058_ $$a3 Experiments -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Results -- 4 Conclusion -- References -- An Explainable Deep Framework: Towards Task-Specific Fusion for Multi-to-One MRI Synthesis -- 1 Introduction -- 2 Methods -- 2.1 Multi-sequence Fusion -- 2.2 Task-Specific Enhanced Map -- 3 Experiments -- 3.1 Dataset and Evaluation Metrics -- 3.2 Implementation Details -- 3.3 Quantitative Results -- 3.4 Ablation Study -- 3.5 Interpretability Visualization -- 4 Conclusion -- References -- Structure-Preserving Synthesis: MaskGAN for Unpaired MR-CT Translation -- 1 Introduction
001481317 5058_ $$a2 Proposed Method -- 2.1 MaskGAN Architecture -- 2.2 CycleGAN Supervision -- 2.3 Mask and Cycle Shape Consistency Supervision -- 3 Experimental Results -- 3.1 Experimental Settings -- 3.2 Results and Discussions -- 4 Conclusion -- References -- Alias-Free Co-modulated Network for Cross-Modality Synthesis and Super-Resolution of MR Images -- 1 Introduction -- 2 Methodology -- 2.1 Co-modulated Network -- 2.2 Alias-Free Generator -- 2.3 Optimization -- 3 Experiments -- 3.1 Experimental Settings -- 3.2 Comparative Experiments -- 4 Conclusion -- References
001481317 5058_ $$aMulti-perspective Adaptive Iteration Network for Metal Artifact Reduction -- 1 Introduction -- 2 Method -- 3 Experiments -- 4 Results -- 5 Discussion and Conclusion -- References -- Noise Conditioned Weight Modulation for Robust and Generalizable Low Dose CT Denoising -- 1 Introduction -- 2 Method -- 3 Experimental Setting -- 4 Result and Discussion -- 5 Conclusion -- References -- Low-Dose CT Image Super-Resolution Network with Dual-Guidance Feature Distillation and Dual-Path Content Communication -- 1 Introduction -- 2 Method -- 2.1 Overall Architecture -- 2.2 Target Function -- 3 Experiments
001481317 506__ $$aAccess limited to authorized users.
001481317 520__ $$aThe ten-volume set LNCS 14220, 14221, 14222, 14223, 14224, 14225, 14226, 14227, 14228, and 14229 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023. The 730 revised full papers presented were carefully reviewed and selected from a total of 2250 submissions. The papers are organized in the following topical sections: Part I: Machine learning with limited supervision and machine learning - transfer learning; Part II: Machine learning -- learning strategies; machine learning -- explainability, bias, and uncertainty; Part III: Machine learning -- explainability, bias and uncertainty; image segmentation; Part IV: Image segmentation; Part V: Computer-aided diagnosis; Part VI: Computer-aided diagnosis; computational pathology; Part VII: Clinical applications -- abdomen; clinical applications -- breast; clinical applications -- cardiac; clinical applications -- dermatology; clinical applications -- fetal imaging; clinical applications -- lung; clinical applications -- musculoskeletal; clinical applications -- oncology; clinical applications -- ophthalmology; clinical applications -- vascular; Part VIII: Clinical applications -- neuroimaging; microscopy; Part IX: Image-guided intervention, surgical planning, and data science; Part X: Image reconstruction and image registration.
001481317 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 3, 2023).
001481317 650_0 $$aDiagnostic imaging$$xData processing$$vCongresses.$$0(DLC)sh2007006024
001481317 655_0 $$aElectronic books.
001481317 7001_ $$aGreenspan, Hayit,$$eeditor.
001481317 7001_ $$aMadabhushi, Anant,$$eeditor.$$1https://orcid.org/0000-0002-5741-0399
001481317 7001_ $$aMousavi, Parvin,$$eeditor.
001481317 7001_ $$aSalcudean, Septimiu Edmund,$$eeditor.$$1https://orcid.org/0000-0001-8826-8025
001481317 7001_ $$aDuncan, James,$$d1951-$$eeditor.$$1https://orcid.org/0000-0002-5167-9856$$0(OCoLC)oca00745149
001481317 7001_ $$aSyeda-Mahmood, Tanveer,$$eeditor.$$1https://orcid.org/0000-0003-0059-3208
001481317 7001_ $$aTaylor, Russell,$$eeditor.$$0(orcid)0000-0001-6272-1100$$1https://orcid.org/0000-0001-6272-1100
001481317 830_0 $$aLecture notes in computer science ;$$v14229.$$x1611-3349
001481317 852__ $$bebk
001481317 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-43999-5$$zOnline Access$$91397441.1
001481317 909CO $$ooai:library.usi.edu:1481317$$pGLOBAL_SET
001481317 980__ $$aBIB
001481317 980__ $$aEBOOK
001481317 982__ $$aEbook
001481317 983__ $$aOnline
001481317 994__ $$a92$$bISE