Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 : 24th International Conference, Strasbourg, France, September 27-October 1, 2021, Proceedings, Part III / edited by Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert.

Format:

Book

Contributor:

Bruijne, Marleen de, Editor.

Cattin, Philippe C., Editor.

Cotin, Stéphane, Editor.

Padoy, Nicolas., Editor.

Speidel, Stefanie, Editor.

Zheng, Yefeng., Editor.

Essert, Caroline, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 12903

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12903

Language:

English

Subjects (All):

Computer vision.

Artificial intelligence.

Pattern recognition systems.

Bioinformatics.

Medical informatics.

Computer Vision.

Artificial Intelligence.

Automated Pattern Recognition.

Computational and Systems Biology.

Health Informatics.

Local Subjects:

Computer Vision.

Artificial Intelligence.

Automated Pattern Recognition.

Computational and Systems Biology.

Health Informatics.

Physical Description:

1 online resource (XXXVI, 648 pages) : 200 illustrations, 185 illustrations in color.

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

System Details:

text file PDF

Summary:

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.

Contents:

Machine Learning - Advances in Machine Learning Theory

Towards Robust General Medical Image Segmentation

Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation

Targeted Gradient Descent: A Novel Method for Convolutional Neural Networks Fine-tuning and Online-learning

A Hierarchical Feature Constraint to CamouflageMedical Adversarial Attacks

Group Shift Pointwise Convolution for Volumetric Medical Image Segmentation

Machine Learning - Attention models

UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation

AlignTransformer: Hierarchical Alignment of Visual Regions and Disease Tags for Medical Report Generation

Continuous-Time Deep Glioma Growth Models

Spine-Transformers: Vertebra Detection and Localization in Arbitrary Field-of-View Spine CT with Transformers

Multi-view analysis of unregistered medical images using cross-view transformers

Machine Learning - Domain Adaptation

Stain Mix-up: Unsupervised Domain Generalization for Histopathology Images

A Unified Hyper-GAN Model for Unpaired Multi-contrast MR Image Translation

Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis

Cooperative Training and Latent Space Data Augmentation for Robust Medical Image Segmentation

Controllable cardiac synthesis via disentangled anatomy arithmetic

CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation

Harmonization with Flow-based Causal Inference

Uncertainty-Aware Label Rectification for Domain Adaptive Mitochondria Segmentation

Semantic Consistent Unsupervised Domain Adaptation for Cross-modality Medical Image Segmentation

Anatomy of Domain Shift Impact on U-Net Layers in MRI Segmentation

FoldIt: Haustral Folds Detection and Segmentation in Colonoscopy Videos

Reference-Relation Guided Autoencoder with Deep CCA Restriction for Awake-to-Sleep Brain Functional Connectome Prediction

Domain Composition and Attention for Unseen-Domain Generalizable Medical Image Segmentation

Fully Test-time Adaptation for Image Segmentation

OLVA: Optimal Latent Vector Alignment for Unsupervised Domain Adaptation in Medical Image Segmentation

Prototypical Interaction Graph for Unsupervised Domain Adaptation in Surgical Instrument Segmentation

Unsupervised Domain Adaptation for Small Bowel Segmentation using Disentangled Representation

Data-driven mapping between functional connectomes using optimal transport

EndoUDA: A modality independent segmentation approach for endoscopy imaging

Style Transfer Using Generative Adversarial Networks for Multi-Site MRI Harmonization

Machine Learning - Federated Learning

Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching

FedPerl: Semi-Supervised Peer Learning for Skin Lesion Classification

Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning

Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures

Federated Contrastive Learning for Volumetric Medical Image Segmentation

Federated Contrastive Learning for Decentralized Unlabeled Medical Images

Machine Learning - Interpretability / Explainability

Explaining COVID-19 and Thoracic Pathology Model Predictions by Identifying Informative Input Features

Demystifying T1-MRI to FDG18-PET Image Translation via Representational Similarity

Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation

An Interpretable Approach to Automated Severity Scoring in Pelvic Trauma

Scalable, Axiomatic Explanations of Deep Alzheimer's Diagnosis from Heterogeneous Data

SPARTA: An Integrated Stability, Discriminability, and Sparsity based Radiomic Feature Selection Approach

The Power of Proxy Data and Proxy Networks for Hyper-Parameter Optimization for Medical Image Segmentation

Fighting Class Imbalance with Contrastive Learning

Interpretable gender classification from retinal fundus images using BagNets

Explainable Classification of Weakly Annotated Wireless Capsule Endoscopy Images based on a Fuzzy Bag-of-Colour Features Model and Brain Storm Optimization

Towards Semantic Interpretation of Thoracic Disease and COVID-19 Diagnosis Models

A Principled Approach to Failure Analysis and Model Repairment: Demonstration in Medical Imaging

Using Causal Analysis for Conceptual Deep Learning Explanation

A spherical convolutional neural network for white matter structure imaging via diffusion MRI

Sharpening Local Interpretable Model-agnostic Explanations for Histopathology: Improved Understandability and Reliability

Improving the Explainability of Skin Cancer Diagnosis Using CBIR

PAC Bayesian Performance Guarantees for (Stochastic) Deep Networks in Medical Imaging

Machine Learning - Uncertainty

Medical Matting: A New Perspective on Medical Segmentation with Uncertainty

Confidence-aware Cascaded Network for Fetal Brain Segmentation on MR Images

Orthogonal Ensemble Networks for Biomedical Image Segmentation

Learning to Predict Error for MRI Reconstruction

Uncertainty-Guided Progressive GANs for Medical Image Translation

Variational Topic Inference for Chest X-Ray Report Generation

Uncertainty Aware Deep Reinforcement Learning for Anatomical Landmark Detection in Medical Images.

Other Format:

Printed edition:

ISBN:

978-3-030-87199-4

9783030871994

Access Restriction:

Restricted for use by site license.