Medical Image Computing and Computer Assisted Intervention – MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part VIIILinwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022. The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology; Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging; Part III: Breast imaging; colonoscopy; computer aided diagnosis; Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I; Part V: Image segmentation II; integration of imaging with non-imaging biomarkers; Part VI: Image registration; image reconstruction; Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning – domain adaptation and generalization; Part VIII: Machine learning – weakly-supervised learning; machine learning – model interpretation; machine learning – uncertainty; machine learning theory and methodologies.
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Contents
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Selfsupervised Fluoroscopy Denoising | 13 |
26em plus 1em minus 1emDiscrepancyBased Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Images | 24 |
Diffusion Models for Medical Anomaly Detection | 35 |
FewShot Generation of Personalized Neural Surrogates for Cardiac Simulation via Bayesian Metalearning | 46 |
Aggregative Selfsupervised Feature Learning from Limited Medical Images | 57 |
Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images | 67 |
Adversarially Robust Prototypical FewShot Segmentation with NeuralODEs | 77 |
Graph Emotion Decoding from Visually Evoked Neural Responses | 396 |
DualGraph Learning Convolutional Networks for Interpretable Alzheimers Disease Diagnosis | 406 |
Asymmetry Disentanglement Network for Interpretable Acute Ischemic Stroke Infarct Segmentation in Noncontrast CT Scans | 416 |
Learning a Semantic Representation Space for Medical Images | 427 |
The debiasing Effect of GANBased Augmentation Methods on Skin Lesion Images | 437 |
Accurate and Explainable ImageBased Prediction Using a Lightweight Generative Model | 448 |
Interpretable Modeling and Reduction of Unknown Errors in Mechanistic Operators | 459 |
Sparse Interpretation of Graph Convolutional Networks for Multimodal Diagnosis of Alzheimers Disease | 469 |
Unified 2D and 3D Selfsupervised Pretraining via Masked Image Modeling Transformer for Ophthalmic Image Classification | 88 |
Selfsupervised Learning of Morphological Representation for 3D EM Segments with ClusterInstance Correlations | 99 |
Calibrating Label Distribution for ClassImbalanced BarelySupervised Knee Segmentation | 109 |
Semisupervised Medical Image Classification with Temporal KnowledgeAware Regularization | 119 |
Selfsupervised Pretraining of Transformers via Human Motion Forecasting for FewShot Gait Impairment Severity Estimation | 130 |
Semisupervised Medical Image Segmentation Using CrossModel PseudoSupervision with Shape Awareness and Local Context Constraints | 140 |
Multitask Selfsupervised Continual Learning to Pretrain Deep Models for XRay Images of Multiple Body Parts | 151 |
A New PU Learning Framework Regularized by Global Consistency for Scribble Supervised Cardiac Segmentation | 162 |
PrototypeAware Contrastive Learning for LongTailed Medical Image Classification | 173 |
Combining MixedFormat Labels for AIBased Pathology Detection Pipeline in a LargeScale Knee MRI Study | 183 |
TaskOriented Selfsupervised Learning for Anomaly Detection in Electroencephalography | 193 |
Multiple Instance Learning with Mixed Supervision in Gleason Grading | 204 |
An Accurate Unsupervised Liver Lesion Detection Method Using Pseudolesions | 214 |
A Study on Cardiac MRI | 224 |
WeaklySupervised Volumetric Image Segmentation via Scribble Annotations | 234 |
Selflearning and OneShot Learning Based SingleSlice Annotation for 3D Medical Image Segmentation | 244 |
Longitudinal Infant Functional Connectivity Prediction via Conditional Intensive Triplet Network | 255 |
Leveraging Labeling Representations in UncertaintyBased Semisupervised Segmentation | 265 |
Analyzing Brain Structural Connectivity as Continuous Random Functions | 276 |
Learning with Context Encoding for SingleStage Cranial Bone Labeling and Landmark Localization | 286 |
Warm Start Active Learning with Proxy Labels and Selection via Semisupervised FineTuning | 297 |
Intervention Interaction Federated Abnormality Detection with Noisy Clients | 309 |
Semisupervised Retinal Layer Segmentation in OCT Using Disentangled Representation with Anatomical Priors | 320 |
PhysiologyBased Simulation of the Retinal Vasculature Enables AnnotationFree Segmentation of OCT Angiographs | 330 |
AnomalyAware Multiple Instance Learning for Rare Anemia Disorder Classification | 341 |
Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation | 351 |
Machine Learning Model Interpretation | 362 |
An Explainable Neural Network Landscape of ReactionDiffusion Model for Cognitive Task Recognition | 365 |
Interpretable Graph Neural Networks for ConnectomeBased Brain Disorder Analysis | 375 |
ConsistencyPreserving Visual Question Answering in Medical Imaging | 386 |
Machine Learning Uncertainty | 479 |
Fusing Two Sources of Uncertainty for Semisupervised Medical Image Segmentation | 480 |
CRISP Reliable Uncertainty Estimation for Medical Image Segmentation | 492 |
Trusted Brain Tumor Segmentation | 503 |
A SinglePass Uncertainty Estimation in Deep Learning for Segmentation | 514 |
Delta Ensemble Uncertainty Estimation for a More Robust Estimation of Ejection Fraction | 525 |
Efficient Bayesian Uncertainty Estimation for nnUNet | 535 |
Improving Trustworthiness of AI Disease Severity Rating in Medical Imaging with Ordinal Conformal Prediction Sets | 545 |
Machine Learning Theory and Methodologies | 555 |
Selfsupervised Poisson Denoising from a Single Image | 557 |
An Inclusive TaskAware Framework for Radiology Report Generation | 568 |
Removal of Confounders via Invariant Risk Minimization for Medical Diagnosis | 578 |
A Selfguided Framework for Radiology Report Generation | 588 |
Counterfactual Video Generation | 599 |
TransformerBased Semantic Query for Medical Report Generation | 610 |
Pseudo BiasBalanced Learning for Debiased Chest XRay Classification | 621 |
Why Patient Data Cannot Be Easily Forgotten? | 632 |
Calibration of Medical Imaging Classification Systems with Weight Scaling | 642 |
Online Reflective Learning for Robust Medical Image Segmentation | 652 |
FineGrained Correlation Loss for Regression | 663 |
Suppressing Poisoning Attacks on Federated Learning for Medical Imaging | 673 |
The Intrinsic Manifolds of Radiological Images and Their Role in Deep Learning | 684 |
Unlearning Scanner Bias with Distributed Data | 695 |
Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models | 705 |
CNNs vs Traditional Model Fitting | 715 |
Adaptive Gradient Triplet Loss with Automatic Margin Learning for Forensic Medical Image Matching | 725 |
The Intrinsic Manifolds of Radiological Images and Their Role in Deep Learning | 736 |
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accuracy achieve algorithm analysis annotations anomaly applied approach arXiv preprint average baseline better bias brain Cham classification clinical compared comparison Computer Computer Vision Conference connectivity consider consistency contains contrastive convolutional dataset deep learning denotes detection Dice disease distribution domain effectiveness embedding encoder error estimation et al evaluate experiments framework function graph IEEE image segmentation improve input instance International labels layer LNCS loss maps mean medical image method metrics MICCAI module Nature neural networks noise normal obtain optimization output parameters patients Pattern performance prediction problem Proceedings proposed Recognition regions representation represents respectively robust samples score segmentation selected semi-supervised shown similar space specific Springer strategy structure subjects supervised Systems Table tasks tion Transformer uncertainty University validation visual volumes Wang weights Zhang
Bibliographic information
| Title | Medical Image Computing and Computer Assisted Intervention – MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part VIII Volume 13438 of Lecture Notes in Computer Science |
| Editors | Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li |
| Publisher | Springer Nature, 2022 |
| ISBN | 3031164520, 9783031164521 |
| Length | 740 pages |
| Subjects | › › Computers / Artificial Intelligence / Computer Vision & Pattern Recognition Computers / Software Development & Engineering / General Technology & Engineering / Electronics / General Technology & Engineering / Imaging Systems |
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