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001481322 001__ 1481322
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001481322 020__ $$a9783031439049$$q(electronic bk.)
001481322 020__ $$a303143904X$$q(electronic bk.)
001481322 020__ $$z9783031439032$$q(print)
001481322 0247_ $$a10.1007/978-3-031-43904-9$$2doi
001481322 035__ $$aSP(OCoLC)1401635294
001481322 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dSFB
001481322 049__ $$aISEA
001481322 050_4 $$aRC78.7.D53$$bI58 2023eb
001481322 08204 $$a616.07/54$$223/eng/20231003
001481322 1112_ $$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$$n(26th :$$d2023 :$$cVancouver, B.C. ; Online)
001481322 24510 $$aMedical image computing and computer assisted intervention -- MICCAI 2023 :$$b26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings.$$nPart V /$$cHayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor, editors.
001481322 2463_ $$aMICCAI 2023
001481322 264_1 $$aCham :$$bSpringer,$$c2023.
001481322 300__ $$a1 online resource (xxxix, 806 pages) :$$billustrations (some color).
001481322 336__ $$atext$$btxt$$2rdacontent
001481322 337__ $$acomputer$$bc$$2rdamedia
001481322 338__ $$aonline resource$$bcr$$2rdacarrier
001481322 4901_ $$aLecture notes in computer science,$$x1611-3349 ;$$v14224
001481322 500__ $$aIncludes author index.
001481322 5050_ $$aIntro -- Preface -- Organization -- Contents - Part III -- Machine Learning - Explainability, Bias, and Uncertainty II -- Pre-trained Diffusion Models for Plug-and-Play Medical Image Enhancement -- 1 Introduction -- 2 Method -- 2.1 Denoising Diffusion Probabilistic Models (DDPM) for Unconditional Image Generation -- 2.2 Image Enhancement with Denoising Algorithm -- 2.3 Pre-Trained Diffusion Models for Plug-and-play Medical Image Enhancement -- 3 Experiments -- 4 Results and Discussion -- 5 Conclusion -- References -- GRACE: A Generalized and Personalized Federated Learning Method for Medical Imaging -- 1 Introduction -- 2 Method -- 2.1 Overview of the GPFL Framework -- 2.2 Local Training Phase: Feature Alignment &amp -- Personalization -- 2.3 Aggregation Phase: Consistency-Enhanced Re-weighting -- 3 Experiments -- 3.1 Dataset and Experimental Setting -- 3.2 Comparison with SOTA Methods -- 3.3 Further Analysis -- 4 Conclusion -- References -- Chest X-ray Image Classification: A Causal Perspective -- 1 Introduction -- 2 Methodology -- 2.1 A Causal View on CXR Images -- 2.2 Causal Intervention via Backdoor Adjustment -- 2.3 Training Object -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Results and Analysis -- 4 Conclusion -- References -- DRMC: A Generalist Model with Dynamic Routing for Multi-center PET Image Synthesis -- 1 Introduction -- 2 Method -- 2.1 Center Interference Issue -- 2.2 Network Architecture -- 2.3 Dynamic Routing Strategy -- 2.4 Loss Function -- 3 Experiments and Results -- 3.1 Dataset and Evaluation -- 3.2 Implementation -- 3.3 Comparative Experiments -- 3.4 Ablation Study -- 4 Conclusion -- References -- Federated Condition Generalization on Low-dose CT Reconstruction via Cross-domain Learning -- 1 Introduction -- 2 Method -- 2.1 iRadonMAP -- 2.2 Proposed FedCG Method -- 3 Experiments -- 3.1 Dataset.
001481322 5058_ $$a3.2 Implementation Details -- 4 Result -- 4.1 Reuslt on Condition #1 -- 4.2 Result on Condition #2 -- 4.3 Ablation Experiments -- 5 Conclusion -- References -- Enabling Geometry Aware Learning Through Differentiable Epipolar View Translation -- 1 Introduction -- 2 Methods -- 3 Experiments -- 3.1 Model Training -- 4 Results -- 5 Discussion and Conclusion -- References -- Enhance Early Diagnosis Accuracy of Alzheimer's Disease by Elucidating Interactions Between Amyloid Cascade and Tau Propagation -- 1 Introduction -- 2 Method -- 2.1 Reaction-Diffusion Model for Neuro-Dynamics -- 2.2 Construction on the Interaction Between Tau and Amyloid -- 2.3 Neural Network Landscape of RDM-Based Dynamic Model -- 3 Experiments -- 3.1 Data Description and Experimental Setting -- 3.2 Ablation Study in Prediction Disease Progression -- 3.3 Prognosis Accuracies on Forecasting AD Risk -- 4 Conclusion -- References -- TauFlowNet: Uncovering Propagation Mechanism of Tau Aggregates by Neural Transport Equation -- 1 Introduction -- 2 Methods -- 2.1 Problem Formulation for Discovering Spreading Flow of Tau Propagation -- 2.2 TauFlowNet: An Explainable Deep Model Principled with TV-Based Lagrangian Mechanics -- 3 Experiments -- 3.1 Evaluate the Prediction Accuracy of Future Tau Accumulation -- 3.2 Examine Spatiotemporal Patterns of the Spreading Flow of Tau Aggregates -- 4 Conclusion -- References -- Uncovering Structural-Functional Coupling Alterations for Neurodegenerative Diseases -- 1 Introduction -- 2 Method -- 2.1 Generalized Kuramoto Model for Coupled Neural Oscillations -- 2.2 Deep Kuramoto Model for SC-FC Coupling Mechanism -- 2.3 Novel SC-FC Coupling Biomarkers -- 3 Experiments -- 3.1 Validating the Neuroscience Insight of Deep Kuramoto Model -- 3.2 Evaluation on Empirical Biomarker of SC-FC-META -- 3.3 Evaluation on SC-FC-Net in Diagnosing AD -- 4 Conclusion.
001481322 5058_ $$a4 Conclusions -- References -- How Reliable are the Metrics Used for Assessing Reliability in Medical Imaging? -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Proposed Metric: Robust Expected Calibration Error (RECE) -- 3.2 Proposed Robust Calibration Regularization (RCR) Loss -- 4 Experiments and Results -- 5 Conclusion -- References -- Co-assistant Networks for Label Correction -- 1 Introduction -- 2 Methodology -- 2.1 Noise Detector -- 2.2 Noise Cleaner -- 2.3 Objective Function -- 3 Experiments -- 3.1 Experimental Settings -- 3.2 Results and Analysis -- 3.3 Ablation Study -- 4 Conclusion -- References -- M3D-NCA: Robust 3D Segmentation with Built-In Quality Control -- 1 Introduction -- 2 Methodology -- 2.1 M3D-NCA Training Pipeline -- 2.2 M3D-NCA Core Architecture -- 2.3 Inherent Quality Control -- 3 Experimental Results -- 3.1 Comparison and Ablation -- 3.2 Automatic Quality Control -- 4 Conclusion -- References -- The Role of Subgroup Separability in Group-Fair Medical Image Classification -- 1 Introduction -- 2 Related Work -- 3 The Role of Subgroup Separability -- 4 Experiments and Results -- 5 Discussion -- References -- Mitigating Calibration Bias Without Fixed Attribute Grouping for Improved Fairness in Medical Imaging Analysis -- 1 Introduction -- 2 Methodology -- 2.1 Training Procedure: Two-Stage Method -- 2.2 Test Time Evaluation on Subgroups of Interest -- 3 Experiments and Results -- 3.1 Results, Ablations, and Analysis -- 4 Conclusions -- References -- SMRD: SURE-Based Robust MRI Reconstruction with Diffusion Models -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Accelerated MRI Reconstruction Using Diffusion Models -- 3.2 Stein's Unbiased Risk Estimator (SURE) -- 3.3 SURE-Based MRI Reconstruction with Diffusion Models -- 4 Experiments -- 5 Results and Discussion -- 6 Conclusion -- References.
001481322 5058_ $$aAsymmetric Contour Uncertainty Estimation for Medical Image Segmentation -- 1 Introduction -- 2 Method -- 2.1 Contouring Uncertainty -- 2.2 Visualization of Uncertainty -- 3 Experimental Setup -- 3.1 Data -- 3.2 Implementation Details -- 3.3 Evaluation Metrics -- 4 Results -- 5 Discussion and Conclusion -- References -- Fourier Test-Time Adaptation with Multi-level Consistency for Robust Classification -- 1 Introduction -- 2 Methodology -- 3 Experimental Results -- 4 Conclusion -- References -- A Model-Agnostic Framework for Universal Anomaly Detection of Multi-organ and Multi-modal Images -- 1 Introduction -- 2 Methodology -- 2.1 Framework Overview -- 2.2 Organ and Modality Classification Constraints -- 2.3 Center Constraint -- 2.4 Optimization and Inference -- 3 Experiments -- 3.1 Experimental Setting -- 3.2 Comparison Study -- 3.3 Ablation Study -- 4 Conclusion -- References -- DiMix: Disentangle-and-Mix Based Domain Generalizable Medical Image Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Framework -- 2.2 Loss Function -- 3 Experiments and Results -- 3.1 Setup -- 3.2 Implementation Details -- 3.3 Results -- 4 Conclusion -- References -- Regular SE(3) Group Convolutions for Volumetric Medical Image Analysis -- 1 Introduction -- 2 Literature Overview -- 3 Separable SE(n) Equivariant Group Convolutions -- 3.1 Regular Group Convolutions -- 3.2 Separable SE(n) Group Convolution -- 4 Experiments and Evaluation -- 4.1 Evaluation Methodology -- 4.2 SE(3) Equivariance Performance -- 4.3 Performance on MedMNIST -- 4.4 Model Generalization -- 4.5 Future Work -- 5 Conclusion -- References -- Deep Learning-Based Anonymization of Chest Radiographs: A Utility-Preserving Measure for Patient Privacy -- 1 Introduction -- 2 Methods -- 2.1 Data -- 2.2 PriCheXy-Net: Adversarial Image Anonymization -- 2.3 Objective Functions -- 3 Experiments and Results.
001481322 506__ $$aAccess limited to authorized users.
001481322 520__ $$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.
001481322 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 3, 2023).
001481322 650_0 $$aDiagnostic imaging$$xData processing$$vCongresses.$$0(DLC)sh2007006024
001481322 655_0 $$aElectronic books.
001481322 7001_ $$aGreenspan, Hayit,$$eeditor.
001481322 7001_ $$aMadabhushi, Anant,$$eeditor.$$1https://orcid.org/0000-0002-5741-0399
001481322 7001_ $$aMousavi, Parvin,$$eeditor.
001481322 7001_ $$aSalcudean, Septimiu Edmund,$$eeditor.$$1https://orcid.org/0000-0001-8826-8025
001481322 7001_ $$aDuncan, James,$$d1951-$$eeditor.$$1https://orcid.org/0000-0002-5167-9856$$0(OCoLC)oca00745149
001481322 7001_ $$aSyeda-Mahmood, Tanveer,$$eeditor.$$1https://orcid.org/0000-0003-0059-3208
001481322 7001_ $$aTaylor, Russell,$$eeditor.$$0(orcid)0000-0001-6272-1100$$1https://orcid.org/0000-0001-6272-1100
001481322 830_0 $$aLecture notes in computer science ;$$v14224.$$x1611-3349
001481322 852__ $$bebk
001481322 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-43904-9$$zOnline Access$$91397441.1
001481322 909CO $$ooai:library.usi.edu:1481322$$pGLOBAL_SET
001481322 980__ $$aBIB
001481322 980__ $$aEBOOK
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