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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 de Marc Aubreville

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: Lecture Notes in Computer Science, cartea 13166

Editat de Marc Aubreville, David Zimmerer, Mattias Heinrich
en Limba Engleză Paperback – 2 mar 2022
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.

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Specificații

ISBN-13: 9783030972806
ISBN-10: 3030972801
Pagini: 194
Ilustrații: IX, 194 p. 68 illus., 51 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.3 kg
Ediția:1st ed. 2022
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Image Processing, Computer Vision, Pattern Recognition, and Graphics

Locul publicării:Cham, Switzerland

Cuprins

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.