| Description |
1 online resource (xxxix, 806 pages) : illustrations (some color) |
| Series |
Lecture notes in computer science, 1611-3349 ; 14224 |
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Lecture notes in computer science ; 14224. 1611-3349
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| Contents |
Intro -- 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 & -- 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 |
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3.2 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 |
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4 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 |
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Asymmetric 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 |
| Summary |
The 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 |
| Notes |
Includes author index |
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Online resource; title from PDF title page (SpringerLink, viewed October 3, 2023) |
| Subject |
Diagnostic imaging -- Data processing -- Congresses
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Diagnostic imaging -- Data processing
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| Genre/Form |
proceedings (reports)
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Conference papers and proceedings
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Conference papers and proceedings.
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Actes de congrès.
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| Form |
Electronic book
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| Author |
Greenspan, Hayit, editor.
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Madabhushi, Anant, editor.
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Mousavi, Parvin, editor
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Salcudean, Septimiu Edmund, editor.
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Duncan, James, 1951- editor.
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Syeda-Mahmood, Tanveer, editor.
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Taylor, Russell, editor.
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| ISBN |
9783031439049 |
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303143904X |
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