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
Herausgegeben:Aubreville, Marc; Zimmerer, David; Heinrich, Mattias

• Broschiertes Buch

Produktbeschreibung

This book constitutes three challenges that were held in conjunction with the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, which was planned to take place in Strasbourg, France but changed to an online event due to the COVID-19 pandemic.

The peer-reviewed 18 long and 9 short papers included in this volume stem from the following three biomedical image analysis challenges:

Mitosis Domain Generalization Challenge (MIDOG 2021), Medical Out-of-Distribution Analysis Challenge (MOOD 2021), and Learn2Reg (L2R 2021).

The challenges share the need for developing and fairly evaluating algorithms that increase accuracy, reproducibility and efficiency of automated image analysis in clinically relevant applications.

Produktdetails

• Lecture Notes in Computer Science 13166
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-030-97280-6
• 1st ed. 2022
• Seitenzahl: 204
• Erscheinungstermin: 2. März 2022
• Englisch
• Abmessung: 235mm x 155mm x 12mm
• Gewicht: 318g
• ISBN-13: 9783030972806
• ISBN-10: 3030972801
• Artikelnr.: 63344969

Inhaltsangabe

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.