Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis
portes grátis
Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis
MICCAI 2021 Challenges: MIDOG 2021, MOOD 2021, and Learn2Reg 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27-October 1, 2021, Proceedings
Zimmerer, David; Heinrich, Mattias; Aubreville, Marc
Springer Nature Switzerland AG
03/2022
194
Mole
Inglês
9783030972806
15 a 20 dias
320
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Preface MIDOG 2021.- Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmainGeneralization Challenge.- Assessing domain adaptation techniques for mitosis detection in multi-scanner breast cancer histopathology images.- Domain-Robust Mitotic Figure Detection with StyleGAN.- Two-step Domain Adaptation for Mitosis Cell Detection in Histopathology Images.- Robust Mitosis Detection Using a Cascade Mask-RCNN Approach With Domain-Specific Residual Cycle-GAN Data Augmentation.- Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge.- MitoDet: Simple and robust mitosis detection.- Multi-source Domain Adaptation Using Gradient Reversal Layer for Mitotic Cell Detection.- Rotation Invariance and Extensive Data Augmentation: a strategy for the Mitosis Domain Generalization (MIDOG) Challenge.- Detecting Mitosis against Domain Shift using a Fused Detector and Deep Ensemble Classi cation Model for MIDOG Challenge.- Domain Adaptive Cascade R-CNN for Mitosis DOmain Generalization (MIDOG) Challenge.- Reducing Domain Shift For Mitosis Detection Using Preprocessing Homogenizers.- Cascade RCNN for MIDOG Challenge.- Sk-Unet Model with Fourier Domain for Mitosis Detection.- Preface MOOD21.- Self-Supervised 3D Out-of-Distribution Detection via Pseudoanomaly Generation.- Self-Supervised Medical Out-of-Distribution Using U-Net Vision Transformers.- SS3D: Unsupervised Out-of-Distribution Detection and Localization for Medical Volumes.- MetaDetector: Detecting Outliers by Learning to Learn from Self-supervision.- AutoSeg - Steering the Inductive Biases for Automatic Pathology Segmentation.- Preface Learn2Reg 2021.- Deformable Registration of Brain MR Images via a Hybrid Loss.- Fraunhofer MEVIS Image Registration Solutions for the Learn2Reg 2021 Challenge.- Unsupervised Volumetric Displacement Fields Using Cost Function Unrolling.- Conditional Deep Laplacian Pyramid Image Registration Network in Learn2Reg Challenge.- TheLearn2Reg 2021 MICCAI Grand Challenge (PIMed Team).- Fast 3D registration with accurate optimisation and little learning for Learn2Reg 2021.- Progressive and Coarse-to-fine Network for Medical Image Registration across Phases, Modalities and Patients. -Semi-supervised Multilevel Symmetric Image Registration Method for Magnetic Resonance Whole Brain Images.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
anomaly localization;artificial intelligence;computer vision;domain shift and generalization;image analysis;image processing;image quality;image reconstruction;image segmentation;machine learning;multi-modality;neural networks;object detection;optimisation;pattern recognition;self-supervised learning;signal detection;signal processing;tumor diagnosis and mitosis detection;unsupervised learning
Preface MIDOG 2021.- Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmainGeneralization Challenge.- Assessing domain adaptation techniques for mitosis detection in multi-scanner breast cancer histopathology images.- Domain-Robust Mitotic Figure Detection with StyleGAN.- Two-step Domain Adaptation for Mitosis Cell Detection in Histopathology Images.- Robust Mitosis Detection Using a Cascade Mask-RCNN Approach With Domain-Specific Residual Cycle-GAN Data Augmentation.- Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge.- MitoDet: Simple and robust mitosis detection.- Multi-source Domain Adaptation Using Gradient Reversal Layer for Mitotic Cell Detection.- Rotation Invariance and Extensive Data Augmentation: a strategy for the Mitosis Domain Generalization (MIDOG) Challenge.- Detecting Mitosis against Domain Shift using a Fused Detector and Deep Ensemble Classi cation Model for MIDOG Challenge.- Domain Adaptive Cascade R-CNN for Mitosis DOmain Generalization (MIDOG) Challenge.- Reducing Domain Shift For Mitosis Detection Using Preprocessing Homogenizers.- Cascade RCNN for MIDOG Challenge.- Sk-Unet Model with Fourier Domain for Mitosis Detection.- Preface MOOD21.- Self-Supervised 3D Out-of-Distribution Detection via Pseudoanomaly Generation.- Self-Supervised Medical Out-of-Distribution Using U-Net Vision Transformers.- SS3D: Unsupervised Out-of-Distribution Detection and Localization for Medical Volumes.- MetaDetector: Detecting Outliers by Learning to Learn from Self-supervision.- AutoSeg - Steering the Inductive Biases for Automatic Pathology Segmentation.- Preface Learn2Reg 2021.- Deformable Registration of Brain MR Images via a Hybrid Loss.- Fraunhofer MEVIS Image Registration Solutions for the Learn2Reg 2021 Challenge.- Unsupervised Volumetric Displacement Fields Using Cost Function Unrolling.- Conditional Deep Laplacian Pyramid Image Registration Network in Learn2Reg Challenge.- TheLearn2Reg 2021 MICCAI Grand Challenge (PIMed Team).- Fast 3D registration with accurate optimisation and little learning for Learn2Reg 2021.- Progressive and Coarse-to-fine Network for Medical Image Registration across Phases, Modalities and Patients. -Semi-supervised Multilevel Symmetric Image Registration Method for Magnetic Resonance Whole Brain Images.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
anomaly localization;artificial intelligence;computer vision;domain shift and generalization;image analysis;image processing;image quality;image reconstruction;image segmentation;machine learning;multi-modality;neural networks;object detection;optimisation;pattern recognition;self-supervised learning;signal detection;signal processing;tumor diagnosis and mitosis detection;unsupervised learning
