Medical Image Computing and Computer Assisted Intervention – MICCAI 2021: 24th International Conference, Strasbourg, France, September 27 – October 1, 2021, Proceedings, Part VIII(12908 Lecture Notes in Computer Science)

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.

Table of Contents:
Clinical Applications - Ophthalmology.- Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition.- Cross-domain Depth Estimation Network for 3D Vessel Reconstruction in OCT Angiography.- Distinguishing Differences Matters: Focal Contrastive Network for Peripheral Anterior Synechiae Recognition.- RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network.- MIL-VT: Multiple Instance Learning Enhanced Vision Transformer for Fundus Image Classification.- Local-global Dual Perception based Deep Multiple Instance Learning for Retinal Disease Classification.- BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Images.- LensID: A CNN-RNN-Based Framework Towards Lens Irregularity Detection in Cataract Surgery Videos.- I-SECRET: Importance-guided fundus image enhancement via semi-supervised contrastive constraining.- Few-shot Transfer Learning for Hereditary Retinal Diseases Recognition.- Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retina OCT Images.- Computational (Integrative) Pathology.- GQ-GCN: Group Quadratic Graph Convolutional Network for Classification of Histopathological Images.- Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network.- Prototypical models for classifying high-risk atypical breast lesions.- Hierarchical Attention Guided Framework for Multi-resolution Collaborative Whole Slide Image Segmentation.- Hierarchical Phenotyping and Graph Modeling of Spatial Architecture in Lymphoid Neoplasms.- A computational geometry approach for modeling neuronal fiber pathways.- TransPath: Transformer-based Self-supervised Learning for Histopathological Image Classification.- From Pixel to Whole Slide: Automatic Detection of Microvascular Invasion in Hepatocellular Carcinoma on Histopathological Image via Cascaded Networks.- DT-MIL: Deformable Transformer for Multi-instance Learning on Histopathological Image.- Early Detection of Liver Fibrosis Using Graph Convolutional Networks.- Hierarchical graph pathomic network for progression free survival prediction.- Increasing Consistency of Evoked Response in Thalamic Nuclei During Repetitive Burst Stimulation of Peripheral Nerve in Humans.- Weakly supervised pan-cancer segmentation tool.- Structure-Preserving Multi-Domain Stain Color Augmentation using Style-Transfer with Disentangled Representations.- MetaCon: Meta Contrastive Learning for Microsatellite Instability Detection.- Generalizing Nucleus Recognition Model in Multi-source Ki67 Immunohistochemistry Stained Images via Domain-specific Pruning.- Cells are Actors: Social Network Analysis with Classical ML for SOTA Histology Image Classification.- Instance-based Vision Transformer for Subtyping of Papillary Renal Cell Carcinoma in Histopathological Image.- Hybrid Supervision Learning for Whole Slide Image Classification.- MorphSet: Improving Renal Histopathology Case Assessment Through Learned Prognostic Vectors.- Accounting for Dependencies in Deep Learning based Multiple Instance Learning for Whole Slide Imaging.- Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks.- Pay Attention with Focus: A Novel Learning Scheme for Classification of Whole Slide Images.- Modalities - Microscopy.- Developmental Stage Classification of Embryos Using Two-Stream Neural Network with Linear-Chain Conditional Random Field.- Semi-supervised Cell Detection in Time-lapse Images Using Temporal Consistency.- Cell Detection in Domain Shift Problem Using Pseudo-Cell-Position Heatmap.- 2D Histology Meets 3D Topology: Cytoarchitectonic Brain Mapping with Graph Neural Networks.- Annotation-efficient Cell Counting.- A Deep Learning Bidirectional Temporal Tracking Algorithm for Automated Blood Cell Counting from Non-invasive Capillaroscopy Videos.- Cell Detection from Imperfect Annotation by Pseudo Label Selection Using P-classification.- Learning Neuron Stitching for Connectomics.- CA^{2.5}-Net Nuclei Segmentation Framework with a Microscopy Cell Benchmark Collection.- Automated Malaria Cells Detection from Blood Smears under Severe Class Imbalance via Importance-aware Balanced Group Softmax.- Non-parametric vignetting correction for sparse spatial transcriptomics images.- Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopy.- Deep Reinforcement Exemplar Learning for Annotation Refinement.- Modalities - Histopathology.- Instance-aware Feature Alignment for Cross-domain Cell Nuclei Detection in Histopathology Images.- Positive-unlabeled Learning for Cell Detection in Histopathology Images with Incomplete Annotations.- GloFlow: Whole Slide Image Stitching from Video using Optical Flow and Global Image Alignment.- Multi-modal Multi-instance Learning using Weakly Correlated Histopathological Images and Tabular Clinical Information.- Ranking loss: A ranking-based deep neural network for colorectal cancer grading in pathology images.- Spatial Attention-based Deep Learning System for Breast Cancer Pathological Complete Response Prediction with Serial Histopathology Images in Multiple Stains.- Integration of Patch Features through Self-Supervised Learning and Transformer for Survival Analysis on Whole Slide Images.- Contrastive Learning Based Stain Normalization Across Multiple Tumor Histopathology.- Semi-supervised Adversarial Learning for Stain Normalisation in Histopathology Images.- Learning Visual Features by Colorization for Slide-Consistent Survival Prediction from Whole Slide Images.- Adversarial learning of cancer tissue representations.- A Multi-attribute Controllable Generative Model for Histopathology Image Synthesis.- Modalities - Ultrasound.- USCL: Pretraining Deep Ultrasound Image Diagnosis Model through Video Contrastive Representation Learning.- Identifying Quantitative and Explanatory Tumor Indexes from Dynamic Contrast Enhanced Ultrasound.- Weakly-Supervised Ultrasound Video Segmentation with Minimal Annotations.- Content-Preserving Unpaired Translation from Simulated to Realistic Ultrasound Images.- Visual-Assisted Probe Movement Guidance for Obstetric Ultrasound Scanning using Landmark Retrieval.- Training Deep Networks for Prostate Cancer Diagnosis Using Coarse Histopathological Labels.- Rethinking Ultrasound Augmentation: A Physics-Inspired Approach.