Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 (eBook, PDF)

24th International Conference, Strasbourg, France, September 27 - October 1, 2021, Proceedings, Part VIII
Redaktion: de Bruijne, Marleen; Essert, Caroline; Zheng, Yefeng; Speidel, Stefanie; Padoy, Nicolas; Cotin, Stéphane; Cattin, Philippe C.

• Format: PDF

Produktbeschreibung

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.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 704
• Erscheinungstermin: 23. September 2021
• Englisch
• ISBN-13: 9783030872373
• Artikelnr.: 62828535

Inhaltsangabe

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.