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Medical Image Computing and Computer Assisted Intervention - MICCAI 2021
24th International Conference, Strasbourg, France, September 27 - October 1, 2021, Proceedings, Part V
1 online resource (XXXVIII, 839 pages 25 illustrations)
The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.* The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III: machine learning - advances in machine learning theory; machine learning - attention models; machine learning - domain adaptation; machine learning - federated learning; machine learning - interpretability / explainability; and machine learning - uncertainty Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality Part V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease prediction Part VI: image reconstruction; clinical applications - cardiac; and clinical applications - vascular Part VII: clinical applications - abdomen; clinical applications - breast; clinical applications - dermatology; clinical applications - fetal imaging; clinical applications - lung; clinical applications - neuroimaging - brain development; clinical applications - neuroimaging - DWI and tractography; clinical applications - neuroimaging - functional brain networks; clinical applications - neuroimaging - others; and clinical applications - oncology Part VIII: clinical applications - ophthalmology; computational (integrative) pathology; modalities - microscopy; modalities - histopathology; and modalities - ultrasound *The conference was held virtually.
Computer Aided Diagnosis -- DeepStationing: Thoracic Lymph Node Station Parsing in CT Scans using Anatomical Context Encoding and Key Organ Auto-Search -- Hepatocellular Carcinoma Segmentation from Digital Subtraction Angiography Videos using Learnable Temporal Difference -- CA-Net: Leveraging Contextual Features for Lung Cancer Prediction -- Semi-Supervised Learning for Bone Mineral Density Estimation in Hip X-ray Images -- DAE-GCN: Identifying Disease-Related Features for Disease Prediction -- Enhanced Breast Lesion Classification via Knowledge Guided Cross-Modal and Semantic Data Augmentation -- Multiple Meta-model Quantifying for Medical Visual Question Answering -- mfTrans-Net: Quantitative Measurement of Hepatocellular Carcinoma via Multi-Function Transformer Regression Network -- You Only Learn Once: Universal Anatomical Landmark Detection -- A Coherent Cooperative Learning Framework Based on Transfer Learning for Unsupervised Cross-domain Classification -- Towards a non-invasive diagnosis of portal hypertension based on an Eulerian CFD model with diffuse boundary conditions -- A Segmentation-Assisted Model for Universal Lesion Detection with Partial Labels -- Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images -- Conditional Training with Bounding Map for Universal Lesion Detection -- Focusing on Clinically Interpretable Features: Selective Attention Regularization for Liver Biopsy Image Classification -- Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image Classification -- Tensor-based Multi-index Representation Learning for Major Depression Disorder Detection with Resting-state fMRI -- Region Ensemble Network for MCI Conversion Prediction With a Relation Regularized Loss -- Airway Anomaly Detection by Graph Neural Network -- Energy-Based Supervised Hashing for Multimorbidity Image Retrieval -- Stochastic 4D Flow Vector-Field Signatures: A new approach for comprehensive 4D Flow MRI quantification -- Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling -- ASC-Net: Adversarial-based Selective Network for Unsupervised Anomaly Segmentation -- Cost-Sensitive Meta-Learning for Progress Prediction of Subjective Cognitive Decline with Brain Structural MRI -- Effective Pancreatic Cancer Screening on Non-contrast CT Scans via Anatomy-Aware Transformers -- Learning from Subjective Ratings Using Auto-Decoded Deep Latent Embeddings -- VertNet: Accurate Vertebra Localization and Identification Network from CT Images -- VinDr-SpineXR: A deep learning framework for spinal lesions detection and classification from radiographs -- Multi-frame Collaboration for Effective Endoscopic Video Polyp Detection via Spatial-Temporal Feature Transformation -- MBFF-Net: Multi-Branch Feature Fusion Network for Carotid Plaque Segmentation in Ultrasound -- Balanced-MixUp for highly imbalanced medical image classification -- Transfer Learning of Deep Spatiotemporal Networks to Model Arbitrarily Long Videos of Seizures -- Retina-Match: Ipsilateral Mammography Lesion Matching in a Single Shot Detection Pipeline -- Towards Robust Dual-view Transformation via Densifying Sparse Supervision for Mammography Lesion Matching -- DeepOPG: Improving Orthopantomogram Finding Summarization with Weak Supervision -- Joint Spinal Centerline Extraction and Curvature Estimation with Row-wise Classification and Curve Graph Network -- LDPolypVideo Benchmark: A Large-scale Colonoscopy Video Dataset of Diverse Polyps -- Continual Learning with Bayesian Model based on a Fixed Pre-trained Feature Extractor -- Alleviating Data Imbalance Issue with Perturbed Input during Inference -- A Deep Reinforced Tree-traversal Agent for Coronary Artery Centerline Extraction -- Sequential Gaussian Process Regression for Simultaneous Pathology Detection and Shape Reconstruction -- Predicting Symptoms from Multiphasic MRI via Multi-Instance Attention Learning for Hepatocellular Carcinoma Grading -- Triplet-Branch Network with Prior-Knowledge Embedding for Fatigue Fracture Grading -- DeepMitral: Fully Automatic 3D Echocardiography Segmentation for Patient Specific Mitral Valve Modelling -- Data Augmentation in Logit Space for Medical Image Classification with Limited Training Data -- Collaborative Image Synthesis and Disease Diagnosis for Classification of Neurodegenerative Disorders with Incomplete Multi-modal Neuroimages -- Seg4Reg+: A Local and Global ConsistencyLearning between Spine Segmentation and CobbAngle Regression -- Meta-Modulation Network for Domain Generalization in Multi-site fMRI Classification -- 3D Brain Midline Delineation for Hematoma Patients -- Unsupervised Representation Learning Meets Pseudo-Label Supervised Self-Distillation: A New Approach to Rare Disease Classification -- nnDetection: A Self-configuring Method for Medical Object Detection -- Automating Embryo Development Stage Detection in Time-Lapse Imaging with Synergic Loss and Temporal Learning -- Deep Neural Dynamic Bayesian Networks applied to EEG sleep spindles modeling -- Few Trust Data Guided Annotation Refinement for Upper Gastrointestinal Anatomy Recognition -- Asymmetric 3D Context Fusion for Universal Lesion Detection -- Detecting Outliers with Poisson Image Interpolation -- MG-NET: Leveraging Pseudo-Imaging for Multi-Modal Metagenome Analysis -- Multimodal Multitask Deep Learning for X-Ray Image Retrieval -- Linear Prediction Residual for Efficient Diagnosis of Parkinson's Disease from Gait -- Primary Tumor and Inter-Organ Augmentations for Supervised Lymph Node Colon Adenocarcinoma Metastasis Detection -- Radiomics-informed Deep Curriculum Learning for Breast Cancer Diagnosis -- Integration of Imaging with Non-Imaging Biomarkers -- Lung Cancer Risk Estimation with Incomplete Data: A Joint Missing Imputation Perspective -- Co-Graph Attention Reasoning based Imaging and Clinical Features Integration for Lymph Node Metastasis Prediction -- Deep Orthogonal Fusion: Multimodal Prognostic Biomarker Discovery Integrating Radiology, Pathology, Genomic, and Clinical Data -- A Novel Bayesian Semi-parametric Model for Learning Heritable Imaging Traits -- Combining 3D Image and Tabular Data via the Dynamic Affine Feature Map Transform -- Image-derived phenotype extraction for genetic discovery via unsupervised deep learning in CMR images -- GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference -- Outcome/Disease Prediction -- Predicting Esophageal Fistula Risks Using a Multimodal Self-Attention Network -- Hybrid Aggregation Network for Survival Analysis from Whole Slide Histopathological Images -- Intracerebral Haemorrhage Growth Prediction Based on Displacement Vector Field and Clinical Metadata -- AMINN: Autoencoder-based Multiple Instance Neural Network Improves Outcome Prediction of Multifocal Liver Metastases -- Survival Prediction Based on Histopathology Imaging and Clinical Data: A Novel, Whole Slide CNN Approach -- Beyond Non-Maximum Suppression - Detecting Lesions in Digital Breast Tomosynthesis Volumes -- A Structural Causal Model MR Images of Multiple Sclerosis -- EMA: Auditing Data Removal from Trained Models -- AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-ray -- Projection-wise Disentangling for Fair and Interpretable Representation Learning: Application to 3D Facial Shape Analysis -- Attention-based Multi-scale Gated Recurrent Encoder with Novel Correlation Loss for COVID-19 Progression Prediction.
edited by Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert.
Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12905