Medical Image Computing and Computer Assisted Intervention – MICCAI 2021
Springer International Publishing (Verlag)
978-3-030-87236-6 (ISBN)
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.
| Erscheinungsdatum | 27.09.2021 |
|---|---|
| Reihe/Serie | Image Processing, Computer Vision, Pattern Recognition, and Graphics | Lecture Notes in Computer Science |
| Zusatzinfo | XXXVIII, 704 p. 227 illus., 213 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 1122 g |
| Themenwelt | Informatik ► Grafik / Design ► Digitale Bildverarbeitung |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Technik | |
| Schlagworte | Applications • Artificial Intelligence • color image processing • Computer Aided Diagnosis • computer assisted interventions • Computer Science • computer vision • conference proceedings • Deep learning • image matching • Image Processing • Image Quality • image reconstruction • Image Segmentation • Informatics • machine learning • Medical Image Analysis • Medical Images • Neural networks • pattern recognition • reference image • Research • Signal Processing |
| ISBN-10 | 3-030-87236-X / 303087236X |
| ISBN-13 | 978-3-030-87236-6 / 9783030872366 |
| Zustand | Neuware |
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