Medical Image Computing and Computer Assisted Intervention – MICCAI 2022

Table of Contents

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   Book Overview
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   Chapter 1 CS $$^2$$ 2 : A Controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Human Intervention
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   Chapter 2 Stabilize, Decompose, and Denoise: Self-supervised Fluoroscopy Denoising
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   Chapter 3 Discrepancy-Based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Images
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   Chapter 4 Diffusion Models for Medical Anomaly Detection
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   Chapter 5 Few-Shot Generation of Personalized Neural Surrogates for Cardiac Simulation via Bayesian Meta-learning
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   Chapter 6 Aggregative Self-supervised Feature Learning from Limited Medical Images
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   Chapter 7 Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images
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   Chapter 8 Adversarially Robust Prototypical Few-Shot Segmentation with Neural-ODEs
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    Chapter 9 Uni4Eye: Unified 2D and 3D Self-supervised Pre-training via Masked Image Modeling Transformer for Ophthalmic Image Classification
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    Chapter 10 Self-supervised Learning of Morphological Representation for 3D EM Segments with Cluster-Instance Correlations
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    Chapter 11 Calibrating Label Distribution for Class-Imbalanced Barely-Supervised Knee Segmentation
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    Chapter 12 Semi-supervised Medical Image Classification with Temporal Knowledge-Aware Regularization
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    Chapter 13 GaitForeMer: Self-supervised Pre-training of Transformers via Human Motion Forecasting for Few-Shot Gait Impairment Severity Estimation
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    Chapter 14 Semi-supervised Medical Image Segmentation Using Cross-Model Pseudo-Supervision with Shape Awareness and Local Context Constraints
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    Chapter 15 MUSCLE: Multi-task Self-supervised Continual Learning to Pre-train Deep Models for X-Ray Images of Multiple Body Parts
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    Chapter 16 ShapePU: A New PU Learning Framework Regularized by Global Consistency for Scribble Supervised Cardiac Segmentation
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    Chapter 17 ProCo: Prototype-Aware Contrastive Learning for Long-Tailed Medical Image Classification
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    Chapter 18 Combining Mixed-Format Labels for AI-Based Pathology Detection Pipeline in a Large-Scale Knee MRI Study
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    Chapter 19 Task-Oriented Self-supervised Learning for Anomaly Detection in Electroencephalography
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    Chapter 20 Multiple Instance Learning with Mixed Supervision in Gleason Grading
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    Chapter 21 An Accurate Unsupervised Liver Lesion Detection Method Using Pseudo-lesions
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    Chapter 22 Addressing Class Imbalance in Semi-supervised Image Segmentation: A Study on Cardiac MRI
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    Chapter 23 Scribble2D5: Weakly-Supervised Volumetric Image Segmentation via Scribble Annotations
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    Chapter 24 Self-learning and One-Shot Learning Based Single-Slice Annotation for 3D Medical Image Segmentation
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    Chapter 25 Longitudinal Infant Functional Connectivity Prediction via Conditional Intensive Triplet Network
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    Chapter 26 Leveraging Labeling Representations in Uncertainty-Based Semi-supervised Segmentation
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    Chapter 27 Analyzing Brain Structural Connectivity as Continuous Random Functions
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    Chapter 28 Learning with Context Encoding for Single-Stage Cranial Bone Labeling and Landmark Localization
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    Chapter 29 Warm Start Active Learning with Proxy Labels and Selection via Semi-supervised Fine-Tuning
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    Chapter 30 Intervention & Interaction Federated Abnormality Detection with Noisy Clients
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    Chapter 31 SD-LayerNet: Semi-supervised Retinal Layer Segmentation in OCT Using Disentangled Representation with Anatomical Priors
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    Chapter 32 Physiology-Based Simulation of the Retinal Vasculature Enables Annotation-Free Segmentation of OCT Angiographs
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    Chapter 33 Anomaly-Aware Multiple Instance Learning for Rare Anemia Disorder Classification
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    Chapter 34 Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation
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    Chapter 35 Neuro-RDM: An Explainable Neural Network Landscape of Reaction-Diffusion Model for Cognitive Task Recognition
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    Chapter 36 Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis
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    Chapter 37 Consistency-Preserving Visual Question Answering in Medical Imaging
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    Chapter 38 Graph Emotion Decoding from Visually Evoked Neural Responses
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    Chapter 39 Dual-Graph Learning Convolutional Networks for Interpretable Alzheimer’s Disease Diagnosis
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    Chapter 40 Asymmetry Disentanglement Network for Interpretable Acute Ischemic Stroke Infarct Segmentation in Non-contrast CT Scans
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    Chapter 41 Regression Metric Loss: Learning a Semantic Representation Space for Medical Images
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    Chapter 42 The (de)biasing Effect of GAN-Based Augmentation Methods on Skin Lesion Images
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    Chapter 43 Accurate and Explainable Image-Based Prediction Using a Lightweight Generative Model
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    Chapter 44 Interpretable Modeling and Reduction of Unknown Errors in Mechanistic Operators
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    Chapter 45 Sparse Interpretation of Graph Convolutional Networks for Multi-modal Diagnosis of Alzheimer’s Disease
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    Chapter 46 FUSSNet: Fusing Two Sources of Uncertainty for Semi-supervised Medical Image Segmentation
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    Chapter 47 CRISP - Reliable Uncertainty Estimation for Medical Image Segmentation
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    Chapter 48 TBraTS: Trusted Brain Tumor Segmentation
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    Chapter 49 Layer Ensembles: A Single-Pass Uncertainty Estimation in Deep Learning for Segmentation
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    Chapter 50 DEUE: Delta Ensemble Uncertainty Estimation for a More Robust Estimation of Ejection Fraction
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    Chapter 51 Efficient Bayesian Uncertainty Estimation for nnU-Net
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    Chapter 52 Improving Trustworthiness of AI Disease Severity Rating in Medical Imaging with Ordinal Conformal Prediction Sets
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    Chapter 53 Poisson2Sparse: Self-supervised Poisson Denoising from a Single Image
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    Chapter 54 An Inclusive Task-Aware Framework for Radiology Report Generation
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    Chapter 55 Removal of Confounders via Invariant Risk Minimization for Medical Diagnosis
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    Chapter 56 A Self-guided Framework for Radiology Report Generation
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    Chapter 57 D’ARTAGNAN: Counterfactual Video Generation
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    Chapter 58 TranSQ: Transformer-Based Semantic Query for Medical Report Generation
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    Chapter 59 Pseudo Bias-Balanced Learning for Debiased Chest X-Ray Classification
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    Chapter 60 Why Patient Data Cannot Be Easily Forgotten?
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    Chapter 61 Calibration of Medical Imaging Classification Systems with Weight Scaling
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    Chapter 62 Online Reflective Learning for Robust Medical Image Segmentation
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    Chapter 63 Fine-Grained Correlation Loss for Regression
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    Chapter 64 Suppressing Poisoning Attacks on Federated Learning for Medical Imaging
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    Chapter 65 The Intrinsic Manifolds of Radiological Images and Their Role in Deep Learning
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    Chapter 66 FedHarmony: Unlearning Scanner Bias with Distributed Data
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    Chapter 67 Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models
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    Chapter 68 Reliability of Quantification Estimates in MR Spectroscopy: CNNs vs Traditional Model Fitting
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    Chapter 69 AdaTriplet: Adaptive Gradient Triplet Loss with Automatic Margin Learning for Forensic Medical Image Matching
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    Chapter 70 Correction to: The Intrinsic Manifolds of Radiological Images and Their Role in Deep Learning