Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 [electronic resource] : 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part II /

Machine Learning - Self-Supervised Learning -- SSLP: Spatial Guided Self-supervised Learning on Pathological Images -- Segmentation of Left Atrial MR Images via Self-supervised Semi-supervised Meta-learning -- Deformed2Self: Self-Supervised Denoising for Dynamic Medical Imaging -- Imbalance-Aware Self-Supervised Learning for 3D Radiomic Representations -- Self-supervised visual representation learning for histopathological images -- Contrastive Learning with Continuous Proxy Meta-Data For 3D MRI Classification -- Sli2Vol: Annotate a 3D Volume from a Single Slice with Self-Supervised Learning -- Self-Supervised Longitudinal Neighbourhood Embedding -- Self-Supervised Multi-Modal Alignment For Whole Body Medical Imaging -- SimTriplet: Simple Triplet Representation Learning with a Single GPU -- Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images -- SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentation -- Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation -- SpineGEM: A Hybrid-Supervised Model Generation Strategy Enabling Accurate Spine Disease Classification with a Small Training Dataset -- Contrastive Learning of Relative Position Regression for One-Shot Object Localization in 3D Medical Images -- Topological Learning and Its Application to Multimodal Brain Network Integration -- One-Shot Medical Landmark Detection -- Implicit field learning for unsupervised anomaly detection in medical images -- Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images -- Contrastive Pre-training and Representation Distillation for Medical Visual Question Answering Based on Radiology Images -- Positional Contrastive Learning for Volumetric Medical Image Segmentation -- Longitudinal self-supervision to disentangle inter-patient variability from disease progression -- Self-Supervised Vessel Enhancement Using Flow-Based Consistencies -- Unsupervised Contrastive Learning of Radiomics and Deep Features for Label-Efficient Tumor Classification -- Learning 4D Infant Cortical Surface Atlas with Unsupervised Spherical Networks -- Multimodal Representation Learning via Maximization of Local Mutual Information -- Inter-Regional High-level Relation Learning from Functional Connectivity via Self-Supervision -- Machine Learning - Semi-Supervised Learning -- Semi-supervised Left Atrium Segmentation with Mutual Consistency Training -- Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation -- Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency -- Few-Shot Domain Adaptation with Polymorphic Transformers -- Lesion Segmentation and RECIST Diameter Prediction via Click-driven Attention and Dual-path Connection -- Reciprocal Learning for Semi-supervised Segmentation -- Disentangled Sequential Graph Autoencoder for Preclinical Alzheimer's Disease Characterizations from ADNI Study -- POPCORN: Progressive Pseudo-labeling with Consistency Regularization and Neighboring -- 3D Semantic Mapping from Arthroscopy using Out-of-distribution Pose and Depth and In-distribution Segmentation Training -- Semi-Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation -- Implicit Neural Distance Representation for Unsupervised and Supervised Classification of Complex Anatomies -- 3D Graph-S2Net: Shape-Aware Self-Ensembling Network for Semi-Supervised Segmentation with Bilateral Graph Convolution -- Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation -- Neighbor Matching for Semi-supervised Learning -- Tripled-uncertainty Guided Mean Teacher model for Semi-supervised Medical Image Segmentation -- Learning with Noise: Mask-guided Attention Model for Weakly Supervised Nuclei Segmentation -- Order-Guided Disentangled Representation Learning for Ulcerative Colitis Classification with Limited Labels -- Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation -- Functional Magnetic Resonance Imaging data augmentation through conditional ICA -- Scalable joint detection and segmentation of surgical instruments with weak supervision -- Machine Learning - Weakly Supervised Learning -- Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss -- Bounding Box Tightness Prior for Weakly Supervised Image Segmentation -- OXnet: Deep Omni-supervised Thoracic Disease Detection from Chest X-rays -- Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation -- Quality-Aware Memory Network for Interactive Volumetric Image Segmentation -- Improving Pneumonia Localization via Cross-Attention on Medical Images and Reports -- Combining Attention-based Multiple Instance Learning and Gaussian Processes for CT Hemorrhage Detection -- CPNet: Cycle Prototype Network for Weakly-supervised 3D Renal Chamber Segmentation -- Observational Supervision for Medical Image Classification using Gaze Data -- Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation -- Efficient and Generic Interactive Segmentation Framework to Correct Mispredictions during Clinical Evaluation of Medical Images -- Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs -- Labels-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation.

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.