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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 VIII
1 online resource (XXXVIII, 704 pages 227 illustrations, 213 illustrations in color.)
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.
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.
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 ; 12908