Medical Image Computing and Computer Assisted Intervention - MICCAI 2023: 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings, Part II.
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
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Cham
Springer
2023
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| Ausgabe: | 1st ed |
| Schriftenreihe: | Lecture Notes in Computer Science Series
v.14221 |
| Schlagworte: | |
| Online-Zugang: | DE-2070s |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (828 Seiten) |
| ISBN: | 9783031438950 |
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| 490 | 0 | |a Lecture Notes in Computer Science Series |v v.14221 | |
| 500 | |a Description based on publisher supplied metadata and other sources | ||
| 505 | 8 | |a Intro -- Preface -- Organization -- Contents - Part II -- Machine Learning - Learning Strategies -- OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology Image Classification -- 1 Introduction -- 2 Method -- 2.1 Framework Overview -- 2.2 Feature-Based Target Sample Selection -- 2.3 Model-Based Informative Sample Selection -- 3 Experiments -- 3.1 Dataset, Settings, Metrics and Competitors -- 3.2 Performance Comparison -- 3.3 Ablation Study -- 4 Conclusion -- References -- SLPT: Selective Labeling Meets Prompt Tuning on Label-Limited Lesion Segmentation -- 1 Introduction -- 2 Methodology -- 2.1 Prompt-Based Visual Model -- 2.2 Diversified Visual Prompt Tuning -- 2.3 Tandem Selective Labeling -- 3 Experiments and Results -- 3.1 Experimental Settings -- 3.2 Results -- 4 Conclusions -- References -- COLosSAL: A Benchmark for Cold-Start Active Learning for 3D Medical Image Segmentation -- 1 Introduction -- 2 COLosSAL Benchmark Definition -- 2.1 3D Medical Image Datasets -- 2.2 Cold-Start AL Scenarios -- 2.3 Baseline Cold-Start Active Learners -- 2.4 Implementation Details -- 3 Experimental Results -- 4 Conclusion -- References -- Continual Learning for Abdominal Multi-organ and Tumor Segmentation -- 1 Introduction -- 2 Methodology -- 2.1 Pseudo Labels for Multi-organ Segmentation -- 2.2 The Proposed Multi-organ Segmentation Model -- 2.3 Computational Complexity Analysis -- 3 Experiment and Result -- 4 Conclusion -- References -- Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI -- 1 Introduction -- 2 Methodology -- 2.1 cBRN Guided Divergence-Aware Decoupled Dual-Flow -- 2.2 HSI Pseudo-label Distillation with Momentum MixUp Decay -- 3 Experiments and Results -- 3.1 Cross-Subset Structure Incremental Evolving -- 3.2 Cross-Modality Structure Incremental Evolving -- 4 Conclusion -- References | |
| 505 | 8 | |a PLD-AL: Pseudo-label Divergence-Based Active Learning in Carotid Intima-Media Segmentation for Ultrasound Images -- 1 Introduction -- 2 Method -- 2.1 Mathematical Notations and Formulation -- 2.2 Outer Loop: Divergence Based AL -- 2.3 Inner Loop: Network Optimization and Label Refinement -- 3 Experiments and Results -- 3.1 Experiment Settings -- 3.2 Performance Comparison -- 3.3 Ablation Study -- 3.4 Application on In-house Dataset -- 4 Conclusion -- References -- Adapter Learning in Pretrained Feature Extractor for Continual Learning of Diseases -- 1 Introduction -- 2 Method -- 2.1 Overall Framework -- 2.2 Task-Specific Adapters -- 2.3 Task-Specific Head -- 3 Experiment Results -- 3.1 Experimental Setup -- 3.2 Result Analysis -- 4 Conclusion -- References -- EdgeAL: An Edge Estimation Based Active Learning Approach for OCT Segmentation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Segmentation Network -- 3.2 Uncertainty in Prediction -- 3.3 Superpixel Selection -- 4 Experiments and Results -- 4.1 Datasets and Networks -- 4.2 Comparisons -- 5 Conclusion -- References -- Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation -- 1 Introduction -- 2 Method -- 2.1 Region-Based Active Learning for WSI Annotation -- 2.2 Region Selection Methods -- 2.3 WSI Semantic Segmentation Framework -- 3 Experiments -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Results -- 4 Discussion and Conclusion -- References -- CXR-CLIP: Toward Large Scale Chest X-ray Language-Image Pre-training -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Data Sampling -- 3.2 Model Architecture -- 3.3 Loss Function -- 4 Experiment -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Comparison with State-of-the-Arts -- 4.4 Ablations -- 5 Conclusion -- References | |
| 505 | 8 | |a VISA-FSS: A Volume-Informed Self Supervised Approach for Few-Shot 3D Segmentation -- 1 Introduction -- 2 Methodology -- 2.1 Problem Setup -- 2.2 Self-supervised Task Generation -- 2.3 Volumetric Segmentation Strategy -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Results and Discussion -- 4 Conclusion -- References -- L3DMC: Lifelong Learning Using Distillation via Mixed-Curvature Space -- 1 Introduction -- 2 Preliminaries -- 3 Proposed Method -- 3.1 Classifier and Exemplar Selection -- 4 Related Work -- 5 Experimental Details -- 6 Results and Discussion -- 7 Conclusion -- References -- Machine Learning - Explainability, Bias, and Uncertainty I -- Weakly Supervised Medical Image Segmentation via Superpixel-Guided Scribble Walking and Class-Wise Contrastive Regularization -- 1 Introduction -- 2 Methods -- 2.1 Preliminaries and Basic Framework -- 2.2 Superpixel-Guided Scribble Walking -- 2.3 Class-Wise Contrastive Regularization -- 3 Experiments and Results -- 4 Conclusion -- References -- SATTA: Semantic-Aware Test-Time Adaptation for Cross-Domain Medical Image Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Test-Time Adaptation Review -- 2.2 Semantic Adaptive Learning Rate -- 2.3 Semantic Proxy Contrastive Learning -- 2.4 Training and Adaptation Procedure -- 3 Experiments -- 3.1 Materials -- 3.2 Comparison with State-of-the-Arts -- 3.3 Ablation Study -- 4 Conclusion -- References -- SFusion: Self-attention Based N-to-One Multimodal Fusion Block -- 1 Introduction -- 2 Methodology -- 2.1 Method Overview -- 2.2 Correlation Extraction -- 2.3 Modal Attention -- 3 Experiments and Results -- 3.1 Datasets -- 3.2 Baseline Methods -- 3.3 Results -- 4 Conclusion -- References -- FedGrav: An Adaptive Federated Aggregation Algorithm for Multi-institutional Medical Image Segmentation -- 1 Introduction -- 2 Method -- 2.1 Overview -- 2.2 FedGrav | |
| 505 | 8 | |a 3 Experiments -- 3.1 Datesets and Settings -- 3.2 Results -- 3.3 Ablation Study -- 4 Conclusion -- References -- Category-Independent Visual Explanation for Medical Deep Network Understanding -- 1 Introduction -- 2 Related Works -- 3 Method -- 4 Experiment -- 4.1 Experiment Setup -- 4.2 Quantitative Evaluation -- 4.3 Qualitative Evaluation -- 4.4 Clinical Application -- 5 Conclusion -- References -- Self-aware and Cross-Sample Prototypical Learning for Semi-supervised Medical Image Segmentation -- 1 Introduction -- 2 Method -- 2.1 Self-cross Prototypical Prediction -- 2.2 Prototypical Prediction Uncertainty -- 2.3 Unsupervised Prototypical Consistency Constraint -- 3 Experiments and Results -- 4 Conclusion -- References -- NeuroExplainer: Fine-Grained Attention Decoding to Uncover Cortical Development Patterns of Preterm Infants -- 1 Introduction -- 2 Method -- 2.1 Spherical Attention Encoding -- 2.2 Hierarchically Spherical Attention Decoding -- 2.3 Domain Knowledge-Guided Explanation Enhancement -- 3 Experiments -- 4 Conclusion -- References -- Centroid-Aware Feature Recalibration for Cancer Grading in Pathology Images -- 1 Introduction -- 2 Methodology -- 2.1 Centroid-Aware Feature Recalibration -- 2.2 Network Architecture -- 3 Experiments and Results -- 3.1 Datasets -- 3.2 Comparative Experiments -- 3.3 Implementation Details -- 3.4 Result and Discussions -- 4 Conclusions -- References -- Federated Uncertainty-Aware Aggregation for Fundus Diabetic Retinopathy Staging -- 1 Introduction -- 2 Methodology -- 2.1 Temperature-Warmed Evidential Uncertainty Head -- 2.2 Uncertainty-Aware Weighting Module -- 3 Loss Function -- 4 Experimental Results -- 5 Conclusion -- References -- Few Shot Medical Image Segmentation with Cross Attention Transformer -- 1 Introduction -- 2 Method -- 2.1 Problem Definition -- 2.2 Network Overview | |
| 505 | 8 | |a 2.3 Mask Incorporated Feature Extraction -- 2.4 Cross Masked Attention Transformer -- 2.5 Iterative Refinement Framework -- 3 Experiment -- 3.1 Dataset and Evaluation Metrics -- 3.2 Implementation Details -- 3.3 Comparison with State-of-the-Art Methods -- 3.4 Ablation Study -- 4 Conclusion -- References -- ECL: Class-Enhancement Contrastive Learning for Long-Tailed Skin Lesion Classification -- 1 Introduction -- 2 Methods -- 2.1 Hybrid-Proxy Model -- 2.2 Balanced-Hybrid-Proxy Loss -- 2.3 Balanced-Weighted Cross-Entropy Loss -- 3 Experiment -- 3.1 Dataset and Implementation Details -- 3.2 Experimental Results -- 4 Conclusion -- References -- Learning Transferable Object-Centric Diffeomorphic Transformations for Data Augmentation in Medical Image Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Object-Centric Diffeomorphism as a Generative Model -- 2.2 Online Augmentations with Generative Models -- 3 Experiments and Results -- 3.1 Generative Model Implementation, Training, and Evaluation -- 3.2 Deformation-Based da for Kidney Tumour Segmentation -- 4 Discussion and Conclusions -- References -- Efficient Subclass Segmentation in Medical Images -- 1 Introduction -- 2 Method -- 3 Experiments -- 4 Conclusion -- References -- Class Specific Feature Disentanglement and Text Embeddings for Multi-label Generalized Zero Shot CXR Classification -- 1 Introduction -- 2 Method -- 2.1 Feature Disentanglement -- 3 Experimental Results -- 3.1 Generalized Zero Shot Learning Results -- 3.2 Ablation Studies -- 4 Conclusion -- References -- Prediction of Cognitive Scores by Joint Use of Movie-Watching fMRI Connectivity and Eye Tracking via Attention-CensNet -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Preprocessing -- 2.3 Classification of Population via Attention-CensNet -- 3 Results -- 3.1 Implementation Details -- 3.2 Ablation Study | |
| 505 | 8 | |a 3.3 Comparison with State-of-the-Arts | |
| 650 | 4 | |a Diagnostic imaging-Data processing-Congresses | |
| 655 | 7 | |0 (DE-588)1071861417 |a Konferenzschrift |y 2023 |z Vancouver |2 gnd-content | |
| 700 | 1 | |a Madabhushi, Anant |e Sonstige |4 oth | |
| 700 | 1 | |a Mousavi, Parvin |e Sonstige |4 oth | |
| 700 | 1 | |a Salcudean, Septimiu |e Sonstige |4 oth | |
| 700 | 1 | |a Duncan, James |e Sonstige |4 oth | |
| 700 | 1 | |a Syeda-Mahmood, Tanveer |e Sonstige |4 oth | |
| 700 | 1 | |a Taylor, Russell |e Sonstige |4 oth | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Greenspan, Hayit |t Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 |d Cham : Springer,c2023 |z 9783031438943 |
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Datensatz im Suchindex
| _version_ | 1820889835402952704 |
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| adam_text | |
| any_adam_object | |
| author | Greenspan, Hayit |
| author_facet | Greenspan, Hayit |
| author_role | aut |
| author_sort | Greenspan, Hayit |
| author_variant | h g hg |
| building | Verbundindex |
| bvnumber | BV050100669 |
| collection | ZDB-30-PQE |
| contents | Intro -- Preface -- Organization -- Contents - Part II -- Machine Learning - Learning Strategies -- OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology Image Classification -- 1 Introduction -- 2 Method -- 2.1 Framework Overview -- 2.2 Feature-Based Target Sample Selection -- 2.3 Model-Based Informative Sample Selection -- 3 Experiments -- 3.1 Dataset, Settings, Metrics and Competitors -- 3.2 Performance Comparison -- 3.3 Ablation Study -- 4 Conclusion -- References -- SLPT: Selective Labeling Meets Prompt Tuning on Label-Limited Lesion Segmentation -- 1 Introduction -- 2 Methodology -- 2.1 Prompt-Based Visual Model -- 2.2 Diversified Visual Prompt Tuning -- 2.3 Tandem Selective Labeling -- 3 Experiments and Results -- 3.1 Experimental Settings -- 3.2 Results -- 4 Conclusions -- References -- COLosSAL: A Benchmark for Cold-Start Active Learning for 3D Medical Image Segmentation -- 1 Introduction -- 2 COLosSAL Benchmark Definition -- 2.1 3D Medical Image Datasets -- 2.2 Cold-Start AL Scenarios -- 2.3 Baseline Cold-Start Active Learners -- 2.4 Implementation Details -- 3 Experimental Results -- 4 Conclusion -- References -- Continual Learning for Abdominal Multi-organ and Tumor Segmentation -- 1 Introduction -- 2 Methodology -- 2.1 Pseudo Labels for Multi-organ Segmentation -- 2.2 The Proposed Multi-organ Segmentation Model -- 2.3 Computational Complexity Analysis -- 3 Experiment and Result -- 4 Conclusion -- References -- Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI -- 1 Introduction -- 2 Methodology -- 2.1 cBRN Guided Divergence-Aware Decoupled Dual-Flow -- 2.2 HSI Pseudo-label Distillation with Momentum MixUp Decay -- 3 Experiments and Results -- 3.1 Cross-Subset Structure Incremental Evolving -- 3.2 Cross-Modality Structure Incremental Evolving -- 4 Conclusion -- References PLD-AL: Pseudo-label Divergence-Based Active Learning in Carotid Intima-Media Segmentation for Ultrasound Images -- 1 Introduction -- 2 Method -- 2.1 Mathematical Notations and Formulation -- 2.2 Outer Loop: Divergence Based AL -- 2.3 Inner Loop: Network Optimization and Label Refinement -- 3 Experiments and Results -- 3.1 Experiment Settings -- 3.2 Performance Comparison -- 3.3 Ablation Study -- 3.4 Application on In-house Dataset -- 4 Conclusion -- References -- Adapter Learning in Pretrained Feature Extractor for Continual Learning of Diseases -- 1 Introduction -- 2 Method -- 2.1 Overall Framework -- 2.2 Task-Specific Adapters -- 2.3 Task-Specific Head -- 3 Experiment Results -- 3.1 Experimental Setup -- 3.2 Result Analysis -- 4 Conclusion -- References -- EdgeAL: An Edge Estimation Based Active Learning Approach for OCT Segmentation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Segmentation Network -- 3.2 Uncertainty in Prediction -- 3.3 Superpixel Selection -- 4 Experiments and Results -- 4.1 Datasets and Networks -- 4.2 Comparisons -- 5 Conclusion -- References -- Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation -- 1 Introduction -- 2 Method -- 2.1 Region-Based Active Learning for WSI Annotation -- 2.2 Region Selection Methods -- 2.3 WSI Semantic Segmentation Framework -- 3 Experiments -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Results -- 4 Discussion and Conclusion -- References -- CXR-CLIP: Toward Large Scale Chest X-ray Language-Image Pre-training -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Data Sampling -- 3.2 Model Architecture -- 3.3 Loss Function -- 4 Experiment -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Comparison with State-of-the-Arts -- 4.4 Ablations -- 5 Conclusion -- References VISA-FSS: A Volume-Informed Self Supervised Approach for Few-Shot 3D Segmentation -- 1 Introduction -- 2 Methodology -- 2.1 Problem Setup -- 2.2 Self-supervised Task Generation -- 2.3 Volumetric Segmentation Strategy -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Results and Discussion -- 4 Conclusion -- References -- L3DMC: Lifelong Learning Using Distillation via Mixed-Curvature Space -- 1 Introduction -- 2 Preliminaries -- 3 Proposed Method -- 3.1 Classifier and Exemplar Selection -- 4 Related Work -- 5 Experimental Details -- 6 Results and Discussion -- 7 Conclusion -- References -- Machine Learning - Explainability, Bias, and Uncertainty I -- Weakly Supervised Medical Image Segmentation via Superpixel-Guided Scribble Walking and Class-Wise Contrastive Regularization -- 1 Introduction -- 2 Methods -- 2.1 Preliminaries and Basic Framework -- 2.2 Superpixel-Guided Scribble Walking -- 2.3 Class-Wise Contrastive Regularization -- 3 Experiments and Results -- 4 Conclusion -- References -- SATTA: Semantic-Aware Test-Time Adaptation for Cross-Domain Medical Image Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Test-Time Adaptation Review -- 2.2 Semantic Adaptive Learning Rate -- 2.3 Semantic Proxy Contrastive Learning -- 2.4 Training and Adaptation Procedure -- 3 Experiments -- 3.1 Materials -- 3.2 Comparison with State-of-the-Arts -- 3.3 Ablation Study -- 4 Conclusion -- References -- SFusion: Self-attention Based N-to-One Multimodal Fusion Block -- 1 Introduction -- 2 Methodology -- 2.1 Method Overview -- 2.2 Correlation Extraction -- 2.3 Modal Attention -- 3 Experiments and Results -- 3.1 Datasets -- 3.2 Baseline Methods -- 3.3 Results -- 4 Conclusion -- References -- FedGrav: An Adaptive Federated Aggregation Algorithm for Multi-institutional Medical Image Segmentation -- 1 Introduction -- 2 Method -- 2.1 Overview -- 2.2 FedGrav 3 Experiments -- 3.1 Datesets and Settings -- 3.2 Results -- 3.3 Ablation Study -- 4 Conclusion -- References -- Category-Independent Visual Explanation for Medical Deep Network Understanding -- 1 Introduction -- 2 Related Works -- 3 Method -- 4 Experiment -- 4.1 Experiment Setup -- 4.2 Quantitative Evaluation -- 4.3 Qualitative Evaluation -- 4.4 Clinical Application -- 5 Conclusion -- References -- Self-aware and Cross-Sample Prototypical Learning for Semi-supervised Medical Image Segmentation -- 1 Introduction -- 2 Method -- 2.1 Self-cross Prototypical Prediction -- 2.2 Prototypical Prediction Uncertainty -- 2.3 Unsupervised Prototypical Consistency Constraint -- 3 Experiments and Results -- 4 Conclusion -- References -- NeuroExplainer: Fine-Grained Attention Decoding to Uncover Cortical Development Patterns of Preterm Infants -- 1 Introduction -- 2 Method -- 2.1 Spherical Attention Encoding -- 2.2 Hierarchically Spherical Attention Decoding -- 2.3 Domain Knowledge-Guided Explanation Enhancement -- 3 Experiments -- 4 Conclusion -- References -- Centroid-Aware Feature Recalibration for Cancer Grading in Pathology Images -- 1 Introduction -- 2 Methodology -- 2.1 Centroid-Aware Feature Recalibration -- 2.2 Network Architecture -- 3 Experiments and Results -- 3.1 Datasets -- 3.2 Comparative Experiments -- 3.3 Implementation Details -- 3.4 Result and Discussions -- 4 Conclusions -- References -- Federated Uncertainty-Aware Aggregation for Fundus Diabetic Retinopathy Staging -- 1 Introduction -- 2 Methodology -- 2.1 Temperature-Warmed Evidential Uncertainty Head -- 2.2 Uncertainty-Aware Weighting Module -- 3 Loss Function -- 4 Experimental Results -- 5 Conclusion -- References -- Few Shot Medical Image Segmentation with Cross Attention Transformer -- 1 Introduction -- 2 Method -- 2.1 Problem Definition -- 2.2 Network Overview 2.3 Mask Incorporated Feature Extraction -- 2.4 Cross Masked Attention Transformer -- 2.5 Iterative Refinement Framework -- 3 Experiment -- 3.1 Dataset and Evaluation Metrics -- 3.2 Implementation Details -- 3.3 Comparison with State-of-the-Art Methods -- 3.4 Ablation Study -- 4 Conclusion -- References -- ECL: Class-Enhancement Contrastive Learning for Long-Tailed Skin Lesion Classification -- 1 Introduction -- 2 Methods -- 2.1 Hybrid-Proxy Model -- 2.2 Balanced-Hybrid-Proxy Loss -- 2.3 Balanced-Weighted Cross-Entropy Loss -- 3 Experiment -- 3.1 Dataset and Implementation Details -- 3.2 Experimental Results -- 4 Conclusion -- References -- Learning Transferable Object-Centric Diffeomorphic Transformations for Data Augmentation in Medical Image Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Object-Centric Diffeomorphism as a Generative Model -- 2.2 Online Augmentations with Generative Models -- 3 Experiments and Results -- 3.1 Generative Model Implementation, Training, and Evaluation -- 3.2 Deformation-Based da for Kidney Tumour Segmentation -- 4 Discussion and Conclusions -- References -- Efficient Subclass Segmentation in Medical Images -- 1 Introduction -- 2 Method -- 3 Experiments -- 4 Conclusion -- References -- Class Specific Feature Disentanglement and Text Embeddings for Multi-label Generalized Zero Shot CXR Classification -- 1 Introduction -- 2 Method -- 2.1 Feature Disentanglement -- 3 Experimental Results -- 3.1 Generalized Zero Shot Learning Results -- 3.2 Ablation Studies -- 4 Conclusion -- References -- Prediction of Cognitive Scores by Joint Use of Movie-Watching fMRI Connectivity and Eye Tracking via Attention-CensNet -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Preprocessing -- 2.3 Classification of Population via Attention-CensNet -- 3 Results -- 3.1 Implementation Details -- 3.2 Ablation Study 3.3 Comparison with State-of-the-Arts |
| ctrlnum | (ZDB-30-PQE)EBC30765501 (ZDB-30-PAD)EBC30765501 (ZDB-89-EBL)EBL30765501 (OCoLC)1401635047 (DE-599)BVBBV050100669 |
| dewey-full | 381 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 381 - Commerce (Trade) |
| dewey-raw | 381 |
| dewey-search | 381 |
| dewey-sort | 3381 |
| dewey-tens | 380 - Commerce, communications, transportation |
| discipline | Wirtschaftswissenschaften |
| edition | 1st ed |
| format | Electronic eBook |
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3D Medical Image Segmentation -- 1 Introduction -- 2 COLosSAL Benchmark Definition -- 2.1 3D Medical Image Datasets -- 2.2 Cold-Start AL Scenarios -- 2.3 Baseline Cold-Start Active Learners -- 2.4 Implementation Details -- 3 Experimental Results -- 4 Conclusion -- References -- Continual Learning for Abdominal Multi-organ and Tumor Segmentation -- 1 Introduction -- 2 Methodology -- 2.1 Pseudo Labels for Multi-organ Segmentation -- 2.2 The Proposed Multi-organ Segmentation Model -- 2.3 Computational Complexity Analysis -- 3 Experiment and Result -- 4 Conclusion -- References -- Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI -- 1 Introduction -- 2 Methodology -- 2.1 cBRN Guided Divergence-Aware Decoupled Dual-Flow -- 2.2 HSI Pseudo-label Distillation with Momentum MixUp Decay -- 3 Experiments and Results -- 3.1 Cross-Subset Structure Incremental Evolving -- 3.2 Cross-Modality Structure Incremental Evolving -- 4 Conclusion -- References</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">PLD-AL: Pseudo-label Divergence-Based Active Learning in Carotid Intima-Media Segmentation for Ultrasound Images -- 1 Introduction -- 2 Method -- 2.1 Mathematical Notations and Formulation -- 2.2 Outer Loop: Divergence Based AL -- 2.3 Inner Loop: Network Optimization and Label Refinement -- 3 Experiments and Results -- 3.1 Experiment Settings -- 3.2 Performance Comparison -- 3.3 Ablation Study -- 3.4 Application on In-house Dataset -- 4 Conclusion -- References -- Adapter Learning in Pretrained Feature Extractor for Continual Learning of Diseases -- 1 Introduction -- 2 Method -- 2.1 Overall Framework -- 2.2 Task-Specific Adapters -- 2.3 Task-Specific Head -- 3 Experiment Results -- 3.1 Experimental Setup -- 3.2 Result Analysis -- 4 Conclusion -- References -- EdgeAL: An Edge Estimation Based Active Learning Approach for OCT Segmentation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Segmentation Network -- 3.2 Uncertainty in Prediction -- 3.3 Superpixel Selection -- 4 Experiments and Results -- 4.1 Datasets and Networks -- 4.2 Comparisons -- 5 Conclusion -- References -- Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation -- 1 Introduction -- 2 Method -- 2.1 Region-Based Active Learning for WSI Annotation -- 2.2 Region Selection Methods -- 2.3 WSI Semantic Segmentation Framework -- 3 Experiments -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Results -- 4 Discussion and Conclusion -- References -- CXR-CLIP: Toward Large Scale Chest X-ray Language-Image Pre-training -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Data Sampling -- 3.2 Model Architecture -- 3.3 Loss Function -- 4 Experiment -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Comparison with State-of-the-Arts -- 4.4 Ablations -- 5 Conclusion -- References</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">VISA-FSS: A Volume-Informed Self Supervised Approach for Few-Shot 3D Segmentation -- 1 Introduction -- 2 Methodology -- 2.1 Problem Setup -- 2.2 Self-supervised Task Generation -- 2.3 Volumetric Segmentation Strategy -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Results and Discussion -- 4 Conclusion -- References -- L3DMC: Lifelong Learning Using Distillation via Mixed-Curvature Space -- 1 Introduction -- 2 Preliminaries -- 3 Proposed Method -- 3.1 Classifier and Exemplar Selection -- 4 Related Work -- 5 Experimental Details -- 6 Results and Discussion -- 7 Conclusion -- References -- Machine Learning - Explainability, Bias, and Uncertainty I -- Weakly Supervised Medical Image Segmentation via Superpixel-Guided Scribble Walking and Class-Wise Contrastive Regularization -- 1 Introduction -- 2 Methods -- 2.1 Preliminaries and Basic Framework -- 2.2 Superpixel-Guided Scribble Walking -- 2.3 Class-Wise Contrastive Regularization -- 3 Experiments and Results -- 4 Conclusion -- References -- SATTA: Semantic-Aware Test-Time Adaptation for Cross-Domain Medical Image Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Test-Time Adaptation Review -- 2.2 Semantic Adaptive Learning Rate -- 2.3 Semantic Proxy Contrastive Learning -- 2.4 Training and Adaptation Procedure -- 3 Experiments -- 3.1 Materials -- 3.2 Comparison with State-of-the-Arts -- 3.3 Ablation Study -- 4 Conclusion -- References -- SFusion: Self-attention Based N-to-One Multimodal Fusion Block -- 1 Introduction -- 2 Methodology -- 2.1 Method Overview -- 2.2 Correlation Extraction -- 2.3 Modal Attention -- 3 Experiments and Results -- 3.1 Datasets -- 3.2 Baseline Methods -- 3.3 Results -- 4 Conclusion -- References -- FedGrav: An Adaptive Federated Aggregation Algorithm for Multi-institutional Medical Image Segmentation -- 1 Introduction -- 2 Method -- 2.1 Overview -- 2.2 FedGrav</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3 Experiments -- 3.1 Datesets and Settings -- 3.2 Results -- 3.3 Ablation Study -- 4 Conclusion -- References -- Category-Independent Visual Explanation for Medical Deep Network Understanding -- 1 Introduction -- 2 Related Works -- 3 Method -- 4 Experiment -- 4.1 Experiment Setup -- 4.2 Quantitative Evaluation -- 4.3 Qualitative Evaluation -- 4.4 Clinical Application -- 5 Conclusion -- References -- Self-aware and Cross-Sample Prototypical Learning for Semi-supervised Medical Image Segmentation -- 1 Introduction -- 2 Method -- 2.1 Self-cross Prototypical Prediction -- 2.2 Prototypical Prediction Uncertainty -- 2.3 Unsupervised Prototypical Consistency Constraint -- 3 Experiments and Results -- 4 Conclusion -- References -- NeuroExplainer: Fine-Grained Attention Decoding to Uncover Cortical Development Patterns of Preterm Infants -- 1 Introduction -- 2 Method -- 2.1 Spherical Attention Encoding -- 2.2 Hierarchically Spherical Attention Decoding -- 2.3 Domain Knowledge-Guided Explanation Enhancement -- 3 Experiments -- 4 Conclusion -- References -- Centroid-Aware Feature Recalibration for Cancer Grading in Pathology Images -- 1 Introduction -- 2 Methodology -- 2.1 Centroid-Aware Feature Recalibration -- 2.2 Network Architecture -- 3 Experiments and Results -- 3.1 Datasets -- 3.2 Comparative Experiments -- 3.3 Implementation Details -- 3.4 Result and Discussions -- 4 Conclusions -- References -- Federated Uncertainty-Aware Aggregation for Fundus Diabetic Retinopathy Staging -- 1 Introduction -- 2 Methodology -- 2.1 Temperature-Warmed Evidential Uncertainty Head -- 2.2 Uncertainty-Aware Weighting Module -- 3 Loss Function -- 4 Experimental Results -- 5 Conclusion -- References -- Few Shot Medical Image Segmentation with Cross Attention Transformer -- 1 Introduction -- 2 Method -- 2.1 Problem Definition -- 2.2 Network Overview</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">2.3 Mask Incorporated Feature Extraction -- 2.4 Cross Masked Attention Transformer -- 2.5 Iterative Refinement Framework -- 3 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Subclass Segmentation in Medical Images -- 1 Introduction -- 2 Method -- 3 Experiments -- 4 Conclusion -- References -- Class Specific Feature Disentanglement and Text Embeddings for Multi-label Generalized Zero Shot CXR Classification -- 1 Introduction -- 2 Method -- 2.1 Feature Disentanglement -- 3 Experimental Results -- 3.1 Generalized Zero Shot Learning Results -- 3.2 Ablation Studies -- 4 Conclusion -- References -- Prediction of Cognitive Scores by Joint Use of Movie-Watching fMRI Connectivity and Eye Tracking via Attention-CensNet -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Preprocessing -- 2.3 Classification of Population via Attention-CensNet -- 3 Results -- 3.1 Implementation Details -- 3.2 Ablation Study</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3.3 Comparison with State-of-the-Arts</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Diagnostic imaging-Data 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| genre | (DE-588)1071861417 Konferenzschrift 2023 Vancouver gnd-content |
| genre_facet | Konferenzschrift 2023 Vancouver |
| id | DE-604.BV050100669 |
| illustrated | Not Illustrated |
| indexdate | 2025-01-10T19:04:28Z |
| institution | BVB |
| isbn | 9783031438950 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035437831 |
| oclc_num | 1401635047 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (828 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Springer |
| record_format | marc |
| series2 | Lecture Notes in Computer Science Series |
| spelling | Greenspan, Hayit Verfasser aut Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings, Part II. 1st ed Cham Springer 2023 ©2023 1 Online-Ressource (828 Seiten) txt rdacontent c rdamedia cr rdacarrier Lecture Notes in Computer Science Series v.14221 Description based on publisher supplied metadata and other sources Intro -- Preface -- Organization -- Contents - Part II -- Machine Learning - Learning Strategies -- OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology Image Classification -- 1 Introduction -- 2 Method -- 2.1 Framework Overview -- 2.2 Feature-Based Target Sample Selection -- 2.3 Model-Based Informative Sample Selection -- 3 Experiments -- 3.1 Dataset, Settings, Metrics and Competitors -- 3.2 Performance Comparison -- 3.3 Ablation Study -- 4 Conclusion -- References -- SLPT: Selective Labeling Meets Prompt Tuning on Label-Limited Lesion Segmentation -- 1 Introduction -- 2 Methodology -- 2.1 Prompt-Based Visual Model -- 2.2 Diversified Visual Prompt Tuning -- 2.3 Tandem Selective Labeling -- 3 Experiments and Results -- 3.1 Experimental Settings -- 3.2 Results -- 4 Conclusions -- References -- COLosSAL: A Benchmark for Cold-Start Active Learning for 3D Medical Image Segmentation -- 1 Introduction -- 2 COLosSAL Benchmark Definition -- 2.1 3D Medical Image Datasets -- 2.2 Cold-Start AL Scenarios -- 2.3 Baseline Cold-Start Active Learners -- 2.4 Implementation Details -- 3 Experimental Results -- 4 Conclusion -- References -- Continual Learning for Abdominal Multi-organ and Tumor Segmentation -- 1 Introduction -- 2 Methodology -- 2.1 Pseudo Labels for Multi-organ Segmentation -- 2.2 The Proposed Multi-organ Segmentation Model -- 2.3 Computational Complexity Analysis -- 3 Experiment and Result -- 4 Conclusion -- References -- Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI -- 1 Introduction -- 2 Methodology -- 2.1 cBRN Guided Divergence-Aware Decoupled Dual-Flow -- 2.2 HSI Pseudo-label Distillation with Momentum MixUp Decay -- 3 Experiments and Results -- 3.1 Cross-Subset Structure Incremental Evolving -- 3.2 Cross-Modality Structure Incremental Evolving -- 4 Conclusion -- References PLD-AL: Pseudo-label Divergence-Based Active Learning in Carotid Intima-Media Segmentation for Ultrasound Images -- 1 Introduction -- 2 Method -- 2.1 Mathematical Notations and Formulation -- 2.2 Outer Loop: Divergence Based AL -- 2.3 Inner Loop: Network Optimization and Label Refinement -- 3 Experiments and Results -- 3.1 Experiment Settings -- 3.2 Performance Comparison -- 3.3 Ablation Study -- 3.4 Application on In-house Dataset -- 4 Conclusion -- References -- Adapter Learning in Pretrained Feature Extractor for Continual Learning of Diseases -- 1 Introduction -- 2 Method -- 2.1 Overall Framework -- 2.2 Task-Specific Adapters -- 2.3 Task-Specific Head -- 3 Experiment Results -- 3.1 Experimental Setup -- 3.2 Result Analysis -- 4 Conclusion -- References -- EdgeAL: An Edge Estimation Based Active Learning Approach for OCT Segmentation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Segmentation Network -- 3.2 Uncertainty in Prediction -- 3.3 Superpixel Selection -- 4 Experiments and Results -- 4.1 Datasets and Networks -- 4.2 Comparisons -- 5 Conclusion -- References -- Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation -- 1 Introduction -- 2 Method -- 2.1 Region-Based Active Learning for WSI Annotation -- 2.2 Region Selection Methods -- 2.3 WSI Semantic Segmentation Framework -- 3 Experiments -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Results -- 4 Discussion and Conclusion -- References -- CXR-CLIP: Toward Large Scale Chest X-ray Language-Image Pre-training -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Data Sampling -- 3.2 Model Architecture -- 3.3 Loss Function -- 4 Experiment -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Comparison with State-of-the-Arts -- 4.4 Ablations -- 5 Conclusion -- References VISA-FSS: A Volume-Informed Self Supervised Approach for Few-Shot 3D Segmentation -- 1 Introduction -- 2 Methodology -- 2.1 Problem Setup -- 2.2 Self-supervised Task Generation -- 2.3 Volumetric Segmentation Strategy -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Results and Discussion -- 4 Conclusion -- References -- L3DMC: Lifelong Learning Using Distillation via Mixed-Curvature Space -- 1 Introduction -- 2 Preliminaries -- 3 Proposed Method -- 3.1 Classifier and Exemplar Selection -- 4 Related Work -- 5 Experimental Details -- 6 Results and Discussion -- 7 Conclusion -- References -- Machine Learning - Explainability, Bias, and Uncertainty I -- Weakly Supervised Medical Image Segmentation via Superpixel-Guided Scribble Walking and Class-Wise Contrastive Regularization -- 1 Introduction -- 2 Methods -- 2.1 Preliminaries and Basic Framework -- 2.2 Superpixel-Guided Scribble Walking -- 2.3 Class-Wise Contrastive Regularization -- 3 Experiments and Results -- 4 Conclusion -- References -- SATTA: Semantic-Aware Test-Time Adaptation for Cross-Domain Medical Image Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Test-Time Adaptation Review -- 2.2 Semantic Adaptive Learning Rate -- 2.3 Semantic Proxy Contrastive Learning -- 2.4 Training and Adaptation Procedure -- 3 Experiments -- 3.1 Materials -- 3.2 Comparison with State-of-the-Arts -- 3.3 Ablation Study -- 4 Conclusion -- References -- SFusion: Self-attention Based N-to-One Multimodal Fusion Block -- 1 Introduction -- 2 Methodology -- 2.1 Method Overview -- 2.2 Correlation Extraction -- 2.3 Modal Attention -- 3 Experiments and Results -- 3.1 Datasets -- 3.2 Baseline Methods -- 3.3 Results -- 4 Conclusion -- References -- FedGrav: An Adaptive Federated Aggregation Algorithm for Multi-institutional Medical Image Segmentation -- 1 Introduction -- 2 Method -- 2.1 Overview -- 2.2 FedGrav 3 Experiments -- 3.1 Datesets and Settings -- 3.2 Results -- 3.3 Ablation Study -- 4 Conclusion -- References -- Category-Independent Visual Explanation for Medical Deep Network Understanding -- 1 Introduction -- 2 Related Works -- 3 Method -- 4 Experiment -- 4.1 Experiment Setup -- 4.2 Quantitative Evaluation -- 4.3 Qualitative Evaluation -- 4.4 Clinical Application -- 5 Conclusion -- References -- Self-aware and Cross-Sample Prototypical Learning for Semi-supervised Medical Image Segmentation -- 1 Introduction -- 2 Method -- 2.1 Self-cross Prototypical Prediction -- 2.2 Prototypical Prediction Uncertainty -- 2.3 Unsupervised Prototypical Consistency Constraint -- 3 Experiments and Results -- 4 Conclusion -- References -- NeuroExplainer: Fine-Grained Attention Decoding to Uncover Cortical Development Patterns of Preterm Infants -- 1 Introduction -- 2 Method -- 2.1 Spherical Attention Encoding -- 2.2 Hierarchically Spherical Attention Decoding -- 2.3 Domain Knowledge-Guided Explanation Enhancement -- 3 Experiments -- 4 Conclusion -- References -- Centroid-Aware Feature Recalibration for Cancer Grading in Pathology Images -- 1 Introduction -- 2 Methodology -- 2.1 Centroid-Aware Feature Recalibration -- 2.2 Network Architecture -- 3 Experiments and Results -- 3.1 Datasets -- 3.2 Comparative Experiments -- 3.3 Implementation Details -- 3.4 Result and Discussions -- 4 Conclusions -- References -- Federated Uncertainty-Aware Aggregation for Fundus Diabetic Retinopathy Staging -- 1 Introduction -- 2 Methodology -- 2.1 Temperature-Warmed Evidential Uncertainty Head -- 2.2 Uncertainty-Aware Weighting Module -- 3 Loss Function -- 4 Experimental Results -- 5 Conclusion -- References -- Few Shot Medical Image Segmentation with Cross Attention Transformer -- 1 Introduction -- 2 Method -- 2.1 Problem Definition -- 2.2 Network Overview 2.3 Mask Incorporated Feature Extraction -- 2.4 Cross Masked Attention Transformer -- 2.5 Iterative Refinement Framework -- 3 Experiment -- 3.1 Dataset and Evaluation Metrics -- 3.2 Implementation Details -- 3.3 Comparison with State-of-the-Art Methods -- 3.4 Ablation Study -- 4 Conclusion -- References -- ECL: Class-Enhancement Contrastive Learning for Long-Tailed Skin Lesion Classification -- 1 Introduction -- 2 Methods -- 2.1 Hybrid-Proxy Model -- 2.2 Balanced-Hybrid-Proxy Loss -- 2.3 Balanced-Weighted Cross-Entropy Loss -- 3 Experiment -- 3.1 Dataset and Implementation Details -- 3.2 Experimental Results -- 4 Conclusion -- References -- Learning Transferable Object-Centric Diffeomorphic Transformations for Data Augmentation in Medical Image Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Object-Centric Diffeomorphism as a Generative Model -- 2.2 Online Augmentations with Generative Models -- 3 Experiments and Results -- 3.1 Generative Model Implementation, Training, and Evaluation -- 3.2 Deformation-Based da for Kidney Tumour Segmentation -- 4 Discussion and Conclusions -- References -- Efficient Subclass Segmentation in Medical Images -- 1 Introduction -- 2 Method -- 3 Experiments -- 4 Conclusion -- References -- Class Specific Feature Disentanglement and Text Embeddings for Multi-label Generalized Zero Shot CXR Classification -- 1 Introduction -- 2 Method -- 2.1 Feature Disentanglement -- 3 Experimental Results -- 3.1 Generalized Zero Shot Learning Results -- 3.2 Ablation Studies -- 4 Conclusion -- References -- Prediction of Cognitive Scores by Joint Use of Movie-Watching fMRI Connectivity and Eye Tracking via Attention-CensNet -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Preprocessing -- 2.3 Classification of Population via Attention-CensNet -- 3 Results -- 3.1 Implementation Details -- 3.2 Ablation Study 3.3 Comparison with State-of-the-Arts Diagnostic imaging-Data processing-Congresses (DE-588)1071861417 Konferenzschrift 2023 Vancouver gnd-content Madabhushi, Anant Sonstige oth Mousavi, Parvin Sonstige oth Salcudean, Septimiu Sonstige oth Duncan, James Sonstige oth Syeda-Mahmood, Tanveer Sonstige oth Taylor, Russell Sonstige oth Erscheint auch als Druck-Ausgabe Greenspan, Hayit Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 Cham : Springer,c2023 9783031438943 |
| spellingShingle | Greenspan, Hayit Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings, Part II. Intro -- Preface -- Organization -- Contents - Part II -- Machine Learning - Learning Strategies -- OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology Image Classification -- 1 Introduction -- 2 Method -- 2.1 Framework Overview -- 2.2 Feature-Based Target Sample Selection -- 2.3 Model-Based Informative Sample Selection -- 3 Experiments -- 3.1 Dataset, Settings, Metrics and Competitors -- 3.2 Performance Comparison -- 3.3 Ablation Study -- 4 Conclusion -- References -- SLPT: Selective Labeling Meets Prompt Tuning on Label-Limited Lesion Segmentation -- 1 Introduction -- 2 Methodology -- 2.1 Prompt-Based Visual Model -- 2.2 Diversified Visual Prompt Tuning -- 2.3 Tandem Selective Labeling -- 3 Experiments and Results -- 3.1 Experimental Settings -- 3.2 Results -- 4 Conclusions -- References -- COLosSAL: A Benchmark for Cold-Start Active Learning for 3D Medical Image Segmentation -- 1 Introduction -- 2 COLosSAL Benchmark Definition -- 2.1 3D Medical Image Datasets -- 2.2 Cold-Start AL Scenarios -- 2.3 Baseline Cold-Start Active Learners -- 2.4 Implementation Details -- 3 Experimental Results -- 4 Conclusion -- References -- Continual Learning for Abdominal Multi-organ and Tumor Segmentation -- 1 Introduction -- 2 Methodology -- 2.1 Pseudo Labels for Multi-organ Segmentation -- 2.2 The Proposed Multi-organ Segmentation Model -- 2.3 Computational Complexity Analysis -- 3 Experiment and Result -- 4 Conclusion -- References -- Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI -- 1 Introduction -- 2 Methodology -- 2.1 cBRN Guided Divergence-Aware Decoupled Dual-Flow -- 2.2 HSI Pseudo-label Distillation with Momentum MixUp Decay -- 3 Experiments and Results -- 3.1 Cross-Subset Structure Incremental Evolving -- 3.2 Cross-Modality Structure Incremental Evolving -- 4 Conclusion -- References PLD-AL: Pseudo-label Divergence-Based Active Learning in Carotid Intima-Media Segmentation for Ultrasound Images -- 1 Introduction -- 2 Method -- 2.1 Mathematical Notations and Formulation -- 2.2 Outer Loop: Divergence Based AL -- 2.3 Inner Loop: Network Optimization and Label Refinement -- 3 Experiments and Results -- 3.1 Experiment Settings -- 3.2 Performance Comparison -- 3.3 Ablation Study -- 3.4 Application on In-house Dataset -- 4 Conclusion -- References -- Adapter Learning in Pretrained Feature Extractor for Continual Learning of Diseases -- 1 Introduction -- 2 Method -- 2.1 Overall Framework -- 2.2 Task-Specific Adapters -- 2.3 Task-Specific Head -- 3 Experiment Results -- 3.1 Experimental Setup -- 3.2 Result Analysis -- 4 Conclusion -- References -- EdgeAL: An Edge Estimation Based Active Learning Approach for OCT Segmentation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Segmentation Network -- 3.2 Uncertainty in Prediction -- 3.3 Superpixel Selection -- 4 Experiments and Results -- 4.1 Datasets and Networks -- 4.2 Comparisons -- 5 Conclusion -- References -- Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation -- 1 Introduction -- 2 Method -- 2.1 Region-Based Active Learning for WSI Annotation -- 2.2 Region Selection Methods -- 2.3 WSI Semantic Segmentation Framework -- 3 Experiments -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Results -- 4 Discussion and Conclusion -- References -- CXR-CLIP: Toward Large Scale Chest X-ray Language-Image Pre-training -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Data Sampling -- 3.2 Model Architecture -- 3.3 Loss Function -- 4 Experiment -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Comparison with State-of-the-Arts -- 4.4 Ablations -- 5 Conclusion -- References VISA-FSS: A Volume-Informed Self Supervised Approach for Few-Shot 3D Segmentation -- 1 Introduction -- 2 Methodology -- 2.1 Problem Setup -- 2.2 Self-supervised Task Generation -- 2.3 Volumetric Segmentation Strategy -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Results and Discussion -- 4 Conclusion -- References -- L3DMC: Lifelong Learning Using Distillation via Mixed-Curvature Space -- 1 Introduction -- 2 Preliminaries -- 3 Proposed Method -- 3.1 Classifier and Exemplar Selection -- 4 Related Work -- 5 Experimental Details -- 6 Results and Discussion -- 7 Conclusion -- References -- Machine Learning - Explainability, Bias, and Uncertainty I -- Weakly Supervised Medical Image Segmentation via Superpixel-Guided Scribble Walking and Class-Wise Contrastive Regularization -- 1 Introduction -- 2 Methods -- 2.1 Preliminaries and Basic Framework -- 2.2 Superpixel-Guided Scribble Walking -- 2.3 Class-Wise Contrastive Regularization -- 3 Experiments and Results -- 4 Conclusion -- References -- SATTA: Semantic-Aware Test-Time Adaptation for Cross-Domain Medical Image Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Test-Time Adaptation Review -- 2.2 Semantic Adaptive Learning Rate -- 2.3 Semantic Proxy Contrastive Learning -- 2.4 Training and Adaptation Procedure -- 3 Experiments -- 3.1 Materials -- 3.2 Comparison with State-of-the-Arts -- 3.3 Ablation Study -- 4 Conclusion -- References -- SFusion: Self-attention Based N-to-One Multimodal Fusion Block -- 1 Introduction -- 2 Methodology -- 2.1 Method Overview -- 2.2 Correlation Extraction -- 2.3 Modal Attention -- 3 Experiments and Results -- 3.1 Datasets -- 3.2 Baseline Methods -- 3.3 Results -- 4 Conclusion -- References -- FedGrav: An Adaptive Federated Aggregation Algorithm for Multi-institutional Medical Image Segmentation -- 1 Introduction -- 2 Method -- 2.1 Overview -- 2.2 FedGrav 3 Experiments -- 3.1 Datesets and Settings -- 3.2 Results -- 3.3 Ablation Study -- 4 Conclusion -- References -- Category-Independent Visual Explanation for Medical Deep Network Understanding -- 1 Introduction -- 2 Related Works -- 3 Method -- 4 Experiment -- 4.1 Experiment Setup -- 4.2 Quantitative Evaluation -- 4.3 Qualitative Evaluation -- 4.4 Clinical Application -- 5 Conclusion -- References -- Self-aware and Cross-Sample Prototypical Learning for Semi-supervised Medical Image Segmentation -- 1 Introduction -- 2 Method -- 2.1 Self-cross Prototypical Prediction -- 2.2 Prototypical Prediction Uncertainty -- 2.3 Unsupervised Prototypical Consistency Constraint -- 3 Experiments and Results -- 4 Conclusion -- References -- NeuroExplainer: Fine-Grained Attention Decoding to Uncover Cortical Development Patterns of Preterm Infants -- 1 Introduction -- 2 Method -- 2.1 Spherical Attention Encoding -- 2.2 Hierarchically Spherical Attention Decoding -- 2.3 Domain Knowledge-Guided Explanation Enhancement -- 3 Experiments -- 4 Conclusion -- References -- Centroid-Aware Feature Recalibration for Cancer Grading in Pathology Images -- 1 Introduction -- 2 Methodology -- 2.1 Centroid-Aware Feature Recalibration -- 2.2 Network Architecture -- 3 Experiments and Results -- 3.1 Datasets -- 3.2 Comparative Experiments -- 3.3 Implementation Details -- 3.4 Result and Discussions -- 4 Conclusions -- References -- Federated Uncertainty-Aware Aggregation for Fundus Diabetic Retinopathy Staging -- 1 Introduction -- 2 Methodology -- 2.1 Temperature-Warmed Evidential Uncertainty Head -- 2.2 Uncertainty-Aware Weighting Module -- 3 Loss Function -- 4 Experimental Results -- 5 Conclusion -- References -- Few Shot Medical Image Segmentation with Cross Attention Transformer -- 1 Introduction -- 2 Method -- 2.1 Problem Definition -- 2.2 Network Overview 2.3 Mask Incorporated Feature Extraction -- 2.4 Cross Masked Attention Transformer -- 2.5 Iterative Refinement Framework -- 3 Experiment -- 3.1 Dataset and Evaluation Metrics -- 3.2 Implementation Details -- 3.3 Comparison with State-of-the-Art Methods -- 3.4 Ablation Study -- 4 Conclusion -- References -- ECL: Class-Enhancement Contrastive Learning for Long-Tailed Skin Lesion Classification -- 1 Introduction -- 2 Methods -- 2.1 Hybrid-Proxy Model -- 2.2 Balanced-Hybrid-Proxy Loss -- 2.3 Balanced-Weighted Cross-Entropy Loss -- 3 Experiment -- 3.1 Dataset and Implementation Details -- 3.2 Experimental Results -- 4 Conclusion -- References -- Learning Transferable Object-Centric Diffeomorphic Transformations for Data Augmentation in Medical Image Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Object-Centric Diffeomorphism as a Generative Model -- 2.2 Online Augmentations with Generative Models -- 3 Experiments and Results -- 3.1 Generative Model Implementation, Training, and Evaluation -- 3.2 Deformation-Based da for Kidney Tumour Segmentation -- 4 Discussion and Conclusions -- References -- Efficient Subclass Segmentation in Medical Images -- 1 Introduction -- 2 Method -- 3 Experiments -- 4 Conclusion -- References -- Class Specific Feature Disentanglement and Text Embeddings for Multi-label Generalized Zero Shot CXR Classification -- 1 Introduction -- 2 Method -- 2.1 Feature Disentanglement -- 3 Experimental Results -- 3.1 Generalized Zero Shot Learning Results -- 3.2 Ablation Studies -- 4 Conclusion -- References -- Prediction of Cognitive Scores by Joint Use of Movie-Watching fMRI Connectivity and Eye Tracking via Attention-CensNet -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Preprocessing -- 2.3 Classification of Population via Attention-CensNet -- 3 Results -- 3.1 Implementation Details -- 3.2 Ablation Study 3.3 Comparison with State-of-the-Arts Diagnostic imaging-Data processing-Congresses |
| subject_GND | (DE-588)1071861417 |
| title | Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings, Part II. |
| title_auth | Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings, Part II. |
| title_exact_search | Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings, Part II. |
| title_full | Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings, Part II. |
| title_fullStr | Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings, Part II. |
| title_full_unstemmed | Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings, Part II. |
| title_short | Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 |
| title_sort | medical image computing and computer assisted intervention miccai 2023 26th international conference vancouver bc canada october 8 12 2023 proceedings part ii |
| title_sub | 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings, Part II. |
| topic | Diagnostic imaging-Data processing-Congresses |
| topic_facet | Diagnostic imaging-Data processing-Congresses Konferenzschrift 2023 Vancouver |
| work_keys_str_mv | AT greenspanhayit medicalimagecomputingandcomputerassistedinterventionmiccai202326thinternationalconferencevancouverbccanadaoctober8122023proceedingspartii AT madabhushianant medicalimagecomputingandcomputerassistedinterventionmiccai202326thinternationalconferencevancouverbccanadaoctober8122023proceedingspartii AT mousaviparvin medicalimagecomputingandcomputerassistedinterventionmiccai202326thinternationalconferencevancouverbccanadaoctober8122023proceedingspartii AT salcudeanseptimiu medicalimagecomputingandcomputerassistedinterventionmiccai202326thinternationalconferencevancouverbccanadaoctober8122023proceedingspartii AT duncanjames medicalimagecomputingandcomputerassistedinterventionmiccai202326thinternationalconferencevancouverbccanadaoctober8122023proceedingspartii AT syedamahmoodtanveer medicalimagecomputingandcomputerassistedinterventionmiccai202326thinternationalconferencevancouverbccanadaoctober8122023proceedingspartii AT taylorrussell medicalimagecomputingandcomputerassistedinterventionmiccai202326thinternationalconferencevancouverbccanadaoctober8122023proceedingspartii |