Description
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. Machine Learning – Advances in Machine Learning Theory.- Towards Robust General Medical Image Segmentation.- Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation.- Targeted Gradient Descent: A Novel Method for Convolutional Neural Networks Fine-tuning and Online-learning.- A Hierarchical Feature Constraint to CamouflageMedical Adversarial Attacks.- Group Shift Pointwise Convolution for Volumetric Medical Image Segmentation.- Machine Learning – Attention models.- UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation.- AlignTransformer: Hierarchical Alignment of Visual Regions and Disease Tags for Medical Report Generation.- Continuous-Time Deep Glioma Growth Models.- Spine-Transformers: Vertebra Detection and Localization in Arbitrary Field-of-View Spine CT with Transformers.- Multi-view analysis of unregistered medical images using cross-view transformers.- Machine Learning – Domain Adaptation.- Stain Mix-up: Unsupervised Domain Generalization for Histopathology Images.- A Unified Hyper-GAN Model for Unpaired Multi-contrast MR Image Translation.- Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis.- Cooperative Training and Latent Space Data Augmentation for Robust Medical Image Segmentation.- Controllable cardiac synthesis via disentangled anatomy arithmetic.- CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation.- Harmonization with Flow-based Causal Inference.- Uncertainty-Aware Label Rectification for Domain Adaptive Mitochondria Segmentation.- Semantic Consistent Unsupervised Domain Adaptation for Cross-modality Medical Image Segmentation.- Anatomy of Domain Shift Impact on U-Net Layers in MRI Segmentation.- FoldIt: Haustral Folds Detection and Segmentation in Colonoscopy Videos.- Reference-Relation Guided Autoencoder with Deep CCA Restriction for Awake-to-Sleep Brain Functional Connectome Prediction.- Domain Composition and Attention for Unseen-Domain Generalizable Medical Image Segmentation.- Fully Test-time Adaptation for Image Segmentation.- OLVA: Optimal Latent Vector Alignment for Unsupervised Domain Adaptation in Medical Image Segmentation.- Prototypical Interaction Graph for Unsupervised Domain Adaptation in Surgical Instrument Segmentation.- Unsupervised Domain Adaptation for Small Bowel Segmentation using Disentangled Representation.- Data-driven mapping between functional connectomes using optimal transport.- EndoUDA: A modality independent segmentation approach for endoscopy imaging.- Style Transfer Using Generative Adversarial Networks for Multi-Site MRI Harmonization.- Machine Learning – Federated Learning.- Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching.- FedPerl: Semi-Supervised Peer Learning for Skin Lesion Classification.- Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning.- Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures.- Federated Contrastive Learning for Volumetric Medical Image Segmentation.- Federated Contrastive Learning for Decentralized Unlabeled Medical Images.- Machine Learning – Interpretability / Explainability.- Explaining COVID-19 and Thoracic Pathology Model Predictions by Identifying Informative Input Features.- Demystifying T1-MRI to FDG18-PET Image Translation via Representational Similarity.- Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation.- An Interpretable Approach to Automated Severity Scoring in Pelvic Trauma.- Scalable, Axiomatic Explanations of Deep Alzheimer’s Diagnosis from Heterogeneous Data.- SPARTA: An Integrated Stability, Discriminability, and Sparsity based Radiomic Feature Selection Approach.- The Power of Proxy Data and Proxy Networks for Hyper-Parameter Optimization for Medical Image Segmentation.- Fighting Class Imbalance with Contrastive Learning.- Interpretable gender classification from retinal fundus images using BagNets.- Explainable Classification of Weakly Annotated Wireless Capsule Endoscopy Images based on a Fuzzy Bag-of-Colour Features Model and Brain Storm Optimization.- Towards Semantic Interpretation of Thoracic Disease and COVID-19 Diagnosis Models.- A Principled Approach to Failure Analysis and Model Repairment: Demonstration in Medical Imaging.- Using Causal Analysis for Conceptual Deep Learning Explanation.- A spherical convolutional neural network for white matter structure imaging via diffusion MRI.- Sharpening Local Interpretable Model-agnostic Explanations for Histopathology: Improved Understandability and Reliability.- Improving the Explainability of Skin Cancer Diagnosis Using CBIR.- PAC Bayesian Performance Guarantees for (Stochastic) Deep Networks in Medical Imaging.- Machine Learning – Uncertainty.- Medical Matting: A New Perspective on Medical Segmentation with Uncertainty.- Confidence-aware Cascaded Network for Fetal Brain Segmentation on MR Images.- Orthogonal Ensemble Networks for Biomedical Image Segmentation.- Learning to Predict Error for MRI Reconstruction.- Uncertainty-Guided Progressive GANs for Medical Image Translation.- Variational Topic Inference for Chest X-Ray Report Generation.- Uncertainty Aware Deep Reinforcement Learning for Anatomical Landmark Detection in Medical Images.
