Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part III

Häftad, Engelska, 2021

AvMarleen de Bruijne,Philippe C. Cattin,Stéphane Cotin,Nicolas Padoy,Stefanie Speidel,Yefeng Zheng,Caroline Essert,Stephane Cotin

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and machine learning - weakly supervised learningPart III: machine learning - advances in machine learning theory; and clinical applications - vascularPart VII: clinical applications - abdomen; and clinical applications - oncologyPart VIII: clinical applications - ophthalmology;

Produktinformation

Utgivningsdatum2021-09-24
Mått155 x 235 x 37 mm
Vikt1 019 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor648
FörlagSpringer Nature Switzerland AG
ISBN9783030871987

Tillhör följande kategorier

Systemvetenskap och AI inom Data och it
Medicinsk bildbehandling inom Medicin
Biovetenskap inom Naturvetenskap och teknik
Tillämpad datateknik inom Data och it

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 ContrastiveLearning.- 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.