Medical Image Computing and Computer Assisted Intervention – MICCAI 2023: 26th International Conference, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, Part II (2025)

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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.

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lgli/7583.pdf

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 : 26th International Conference, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, Part II

Alternative publisher

Springer Nature Switzerland AG

Alternative edition

Springer Nature, Cham, 2023

Alternative edition

Switzerland, Switzerland

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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
4 Conclusion
References
Partial Vessels Annotation-Based Coronary Artery Segmentation with Self-training and Prototype Learning
1 Introduction
2 Method
2.1 Local Feature Extraction Stage
2.2 Global Structure Reconstruction Stage
3 Experiments and Results
3.1 Dataset and Evaluation Metrics
3.2 Implementation Details
3.3 Comparative Test
3.4 Ablation Study
4 Conclusion
References
FairAdaBN: Mitigating Unfairness with Adaptive Batch Normalization and Its Application to Dermatological Disease Classification
1 Introduction
2 Related Work
3 FairAdaBN
4 Experiments and Results
4.1 Evaluation Metrics
4.2 Dataset and Network Configuration
4.3 Results
4.4 Ablation Study
5 Conclusion
References
FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation
1 Introduction
2 Method
2.1 Problem Setup
2.2 Generalization and Flat Minima
2.3 Our Solution: FedSoup
3 Experiments
3.1 Experimental Setup
3.2 Comparison with State-of-the-Art Methods
3.3 Unseen Domain Generalization
4 Conclusion
References
TransLiver: A Hybrid Transformer Model for Multi-phase Liver Lesion Classification
1 Introduction
2 Method
2.1 Pre-processing Unit
2.2 Convolutional Encoder and Convolutional Down-Sampler
2.3 Single-Phase Liver Transformer Block
2.4 Multi-phase Liver Transformer Block
3 Experiments
3.1 Liver Lesion Classification
3.2 Ablation Study
4 Conclusion
References
ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic Diffusion Models
1 Introduction
2 Method
2.1 Mask Conditioning
2.2 Adaptive Loss Re-weighting
2.3 Prediction-Guided Sample Refinement
3 Experiments
3.1 ArSDM Experimental Settings
3.2 Downstream Experimental Settings
3.3 Quantitative Comparisons
3.4 Qualitative Analyses
4 Conclusion
References
FeSViBS: Federated Split Learning of Vision Transformer with Block Sampling
1 Introduction
2 Methodology
2.1 FeSViBS Framework
3 Experimental Setup
4 Results and Analysis
5 Ablation Study
6 Conclusion and Future Directions
References
Localized Questions in Medical Visual Question Answering
1 Introduction
2 Method
3 Experiments and Results
3.1 Datasets
3.2 Baselines and Metrics
3.3 Results
4 Conclusions
References
Reconstructing the Hemodynamic Response Function via a Bimodal Transformer
1 Introduction
2 Data
3 Method
4 Experiments
5 Conclusions and Future Work
References
Debiasing Medical Visual Question Answering via Counterfactual Training
1 Introduction
2 Methodology
2.1 Counterfactual Training Data Preparation
2.2 Counterfactual Cause Effect Training Procedure
3 SLAKE-CP: Construction and Analysis
4 Experiments
4.1 Datasets and Implementation Details
4.2 Experimental Results
5 Conclusion
References
Spatiotemporal Hub Identification in Brain Network by Learning Dynamic Graph Embedding on Grassmannian Manifold
1 Introduction
2 Method
2.1 Dynamic Graph Embedding Learning
2.2 Optimization on Grassmannian Manifold
3 Experiments and Results
3.1 Accuracy and Robustness on Synthesized Network Data
3.2 Evaluation of Hub Identification on Real Brain Networks
4 Conclusion
References
Towards AI-Driven Radiology Education: A Self-supervised Segmentation-Based Framework for High-Precision Medical Image Editing
1 Introduction
2 Methodology
2.1 First Training Stage for Self-supervised Segmentation
2.2 Second Training Stage for Faithful Image Synthesis
2.3 Inference Stage for Medical Image Editing
3 Experiments and Results
4 Conclusion
References
Rethinking Semi-Supervised Federated Learning: How to Co-train Fully-Labeled and Fully-Unlabeled Client Imaging Data
1 Introduction
2 Methods
2.1 Problem Description
2.2 Local Training
2.3 Isolated Federated Aggregation
2.4 Client-Adaptive Pretraining
3 Experiments and Results
3.1 Datasets and FL Settings
3.2 Implementation and Training Details
3.3 Results and Discussion
3.4 Ablation Study
4 Conclusion
References
Right for the Wrong Reason: Can Interpretable ML Techniques Detect Spurious Correlations?
1 Introduction
2 A Framework for Evaluating Explanation Techniques
2.1 Evaluation Strategy
2.2 Studied Confounders
2.3 Evaluation Metrics for Measuring Confounder Detection
2.4 Evaluated Explanation Methods
3 Results
4 Discussion
References
Interpretable Medical Image Classification Using Prototype Learning and Privileged Information
1 Introduction
2 Methods
3 Experiments
4 Results
5 Discussion and Conclusion
References
Physics-Based Decoding Improves Magnetic Resonance Fingerprinting
1 Introduction
2 Background and Related Works
3 Problem Formulation and Method
3.1 BlochNet: Regularized Networks by Physics-Based Decoding
3.2 Fast EPG for Solving Bloch Equations
3.3 Loss Function
4 Experiment Results
4.1 Data Settings and Baseline Methods
4.2 Experiments of Evaluating Generalization Performance
5 Discussion
6 Conclusion
References
Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentation
1 Introduction
2 Frequency Domain Adversarial Attack and Training
2.1 Volumetric Adversarial Frequency Attack (VAFA)
2.2 Volumetric Adversarial Frequency Training (VAFT)
3 Experiments and Results
4 Conclusion
References
Localized Region Contrast for Enhancing Self-supervised Learning in Medical Image Segmentation
1 Introduction
2 Methodology
2.1 Pre-training Stage
2.2 Fine-Tuning Stage
3 Experimental Results
3.1 Pre-training Dataset
3.2 Fine-Tuning Datasets
3.3 Implementation Details
3.4 Quantitative Results
3.5 Qualitative Results
3.6 Visualization of Localized Regions
3.7 Ablation Study
4 Conclusion
References
A Spatial-Temporal Deformable Attention Based Framework for Breast Lesion Detection in Videos
1 Introduction
2 Method
2.1 Spatial-Temporal Deformable Attention
2.2 Spatial-Temporal Deformable Attention Based Encoder and Decoder
2.3 Multi-frame Prediction with Encoder Feature Shuffle
3 Experiments
3.1 Dataset and Implementation Details
3.2 State-of-the-Art Comparison
3.3 Ablation Study
4 Conclusion
References
A Flexible Framework for Simulating and Evaluating Biases in Deep Learning-Based Medical Image Analysis
1 Introduction
2 Methods
3 Experiments and Results
4 Conclusion
References
Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical Imaging
1 Introduction
2 Method
2.1 Preliminaries
2.2 Adaptive Intermediary for Improving Client-Level DP
2.3 Cumulation of Sample-Level DP to Client-Level
3 Experiment
3.1 Experimental Setup
3.2 Empirical Evaluation
3.3 Analytical Studies
4 Conclusion
References
Inflated 3D Convolution-Transformer for Weakly-Supervised Carotid Stenosis Grading with Ultrasound Videos
1 Introduction
2 Methodology
3 Experimental Results
4 Conclusion
References
One-Shot Federated Learning on Medical Data Using Knowledge Distillation with Image Synthesis and Client Model Adaptation
1 Introduction
2 Method
3 Experiments
3.1 Main Results
4 Conclusion
References
Multi-objective Point Cloud Autoencoders for Explainable Myocardial Infarction Prediction
1 Introduction
2 Methods
2.1 Dataset and Preprocessing
2.2 Network Architecture
2.3 Loss and Training
3 Experiments and Results
3.1 Input Shape Reconstruction
3.2 Myocardial Infarction Prediction
3.3 Task-Specific Latent Space Analysis
3.4 Ablation Study
4 Discussion and Conclusion
References
Aneurysm Pose Estimation with Deep Learning
1 Introduction
2 Materiels and Methods
2.1 Datasets and Data Annotation
2.2 Data Sampling and Generation
2.3 Neural Network Architecture
2.4 Loss Function
2.5 Implementation Details
2.6 Evaluation Metrics
3 Experiments and Results
3.1 Pose Estimation
3.2 Object Detection
4 Conclusion
References
Joint Optimization of a -VAE for ECG Task-Specific Feature Extraction
1 Introduction
2 Methods
2.1 Data
2.2 Data Preprocessing
2.3 Model Overview
2.4 Model Training
2.5 Feature Evaluation
2.6 Baseline Methods
3 Experiments and Results
3.1 Experiments
3.2 Hyperparameter Optimization
3.3 Results on the Test Set
4 Discussion
References
Adaptive Multi-scale Online Likelihood Network for AI-Assisted Interactive Segmentation
1 Introduction
2 Method
2.1 Multi-scale Online Likelihood Network
2.2 Adaptive Loss for Online Learning
2.3 Improving Efficiency with Probability-Guided Pruning
3 Experimental Validation
3.1 Quantitative Comparison Using Synthetic Scribbler
3.2 Performance and Workload Validation by Expert User
4 Conclusion
References
Explainable Image Classification with Improved Trustworthiness for Tissue Characterisation
1 Introduction
2 Methodology
3 Experiments and Analysis
4 Conclusion
References
A Video-Based End-to-end Pipeline for Non-nutritive Sucking Action Recognition and Segmentation in Young Infants
1 Introduction
2 Related Work
3 Infant NNS Action Recognition and Segmentation
3.1 NNS Action Recognition
3.2 NNS Action Segmentation
4 Experiments, Results, and Ablation Study
4.1 NNS Dataset Creation
4.2 NNS Recognition Implementation and Results
4.3 NNS Segmentation Results
5 Conclusion
References
Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models
1 Introduction
2 Related Work
3 Reveal to Revise Framework
3.1 Data Artifact Identification and Localization
3.2 Methods for Model Correction
4 Experiments
4.1 Experimental Setup
4.2 Revealing and Revising Spurious Model Behavior
4.3 Iterative Model Correction with R2R
5 Conclusion
References
Faithful Synthesis of Low-Dose Contrast-Enhanced Brain MRI Scans Using Noise-Preserving Conditional GANs
1 Introduction
2 Methodology
2.1 Conditional GANs for Contrast Signal Synthesis
2.2 Noise-Preserving Content Loss
3 Numerical Results
4 Conclusions
References
Prediction of Infant Cognitive Development with Cortical Surface-Based Multimodal Learning
1 Introduction
2 Method
2.1 Surface-Based Fine-Grained Information Representation
2.2 Modality-Specific Encoder
2.3 Modality-Fusion Block
2.4 Cognitive Scores Prediction
3 Experiments
3.1 Dataset
3.2 Experimental Settings
3.3 Results
4 Conclusion
References
Distilling BlackBox to Interpretable Models for Efficient Transfer Learning
1 Introduction
2 Methodology
2.1 Distilling BB to the Mixture of Interpretable Models
2.2 Finetuning to an Unseen Domain
3 Experiments
4 Conclusion
References
Gadolinium-Free Cardiac MRI Myocardial Scar Detection by 4D Convolution Factorization
1 Introduction
2 Methods
2.1 Spatiotemporal Decomposition Using 4D(3D+time) Layers
2.2 Residual Attention Blocks
2.3 Network Details
3 Materials and Implementation Details
4 Experiments and Results
5 Discussion
6 Conclusion
References
Longitudinal Multimodal Transformer Integrating Imaging and Latent Clinical Signatures from Routine EHRs for Pulmonary Nodule Classification
1 Introduction
2 Methods
3 Experimental Setup
4 Results
5 Discussion and Conclusion
References
FedContrast-GPA: Heterogeneous Federated Optimization via Local Contrastive Learning and Global Process-Aware Aggregation
1 Introduction
2 Method
2.1 Intra- and Inter-client Local-Prototype based Contrastive Learning
2.2 Process-Aware Global Model Aggregation
3 Experiments and Results
4 Conclusions
References
Partially Supervised Multi-organ Segmentation via Affinity-Aware Consistency Learning and Cross Site Feature Alignment
1 Introduction
2 Methodology
2.1 Preliminaries
2.2 Prototype Generation
2.3 Affinity-Aware Consistency Learning
2.4 Cross-Site Feature Alignment (CSFA) Module
3 Experiments and Results
4 Conclusion
References
Attentive Deep Canonical Correlation Analysis for Diagnosing Alzheimer's Disease Using Multimodal Imaging Genetics
1 Introduction
2 Method
3 Experiments and Results
3.1 Data Acquisition and Preprocessing
3.2 Evaluation of Disease Classification Performance
3.3 The Most Discriminative Brain Regions and SNPs
3.4 Ablation Study
3.5 Hyperparameter Analysis
4 Conclusion
References
FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classification
1 Introduction
2 Methodology
2.1 Preliminaries and Overview
2.2 Intra-Client Contrastive Learning
2.3 Inter-client Contrastive Learning
2.4 Difficulty-Aware Logit Adjustment
3 Experiments
3.1 Comparison with State-of-the-Art Methods
3.2 Ablation Study
4 Conclusion
References
Transferability-Guided Multi-source Model Adaptation for Medical Image Segmentation
1 Introduction
2 Method
2.1 Label-Free Transferability Metric
2.2 Transferability-Guided Model Adaptation
3 Experiment
3.1 Dataset
3.2 Implementation Details and Evaluation Metrics
3.3 Comparison with State-of-the-Arts
3.4 Ablation Analysis
4 Conclusion
References
Explaining Massive-Training Artificial Neural Networks in Medical Image Analysis Task Through Visualizing Functions Within the Models
1 Introduction
2 Method
2.1 MTANN Deep Learning
2.2 Sensitivity-Based Structure Optimization
2.3 Calculation of Weighted Function Maps
2.4 Unsupervised Hierarchical Clustering
3 Experiments
3.1 Dynamic Contrast-Enhanced Liver CT
4 Conclusion
References
An Explainable Geometric-Weighted Graph Attention Network for Identifying Functional Networks Associated with Gait Impairment
1 Introduction
2 XGW-GAT: Explainable, Geometric-Weighted GAT
2.1 Connectomes in a Riemannian Manifold
2.2 Stratified Learning-Based Sample Selection
2.3 Dynamic Graph Attention Layers
2.4 Individual- And Global-Level Explanations
3 Experiments
3.1 Results
4 Discussion
5 Conclusion
References
An Interpretable and Attention-Based Method for Gaze Estimation Using Electroencephalography
1 Introduction
2 Model
2.1 Motivation
2.2 Attention-CNN
3 Experiments and Results
3.1 Materials and Experimental Settings
3.2 Performance of the Attention-CNN
3.3 Model Interpretability by Case Studies
3.4 Explainability Quantification
4 Conclusion
References
On the Relevance of Temporal Features for Medical Ultrasound Video Recognition
1 Introduction and Related Work
2 Proposed Method
2.1 USVN
2.2 Benchmark Implementations
3 Experimental Results
3.1 Datasets
3.2 Results
3.3 Implementation Details
4 Conclusions and Discussion
References
Synthetic Augmentation with Large-Scale Unconditional Pre-training
1 Introduction
2 Methodology
2.1 Diffusion Models
2.2 HistoDiffusion
3 Experiments
4 Conclusions
References
DeDA: Deep Directed Accumulator
1 Introduction
2 Methodology
2.1 Differentiable Directed Accumulation
2.2 DeDA-Based Transformation Layer for Rim Parameterization
3 Experiments and Results
3.1 Comparator Methods and Implementation Details
3.2 Results and Ablation Study
3.3 Discussions
4 Conclusions
References
Mixing Temporal Graphs with MLP for Longitudinal Brain Connectome Analysis
1 Introduction
2 STGMLP: Spatio-Temporal Graph MLP
3 Experiments
3.1 Materials and Setup
3.2 Evaluation and Discussions on the Results
3.3 Temporal Analysis on AD-Specific Activation
3.4 Ablation Study on Hyperparamters
4 Conclusion
References
Correction to: COLosSAL: A Benchmark for Cold-Start Active Learning for 3D Medical Image Segmentation
Correction to: Chapter “COLosSAL: A Benchmark for Cold-Start Active Learning for 3D Medical Image Segmentation” in: H. Greenspan et al. (Eds.): Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, LNCS 14221, https://doi.org/10.1007/978-3-031-43895-0_3
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2024-03-31

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