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 / edited by Marc Aubreville, David Zimmerer, Mattias Heinrich.

Format:

Book

Contributor:

Aubreville, Marc, Editor.

Zimmerer, David., Editor.

Heinrich, Mattias., Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 13166

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 13166

Language:

English

Subjects (All):

Image processing-Digital techniques.

Computer vision.

Computers.

Application software.

Machine learning.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Computing Milieux.

Computer and Information Systems Applications.

Machine Learning.

Local Subjects:

Computer Imaging, Vision, Pattern Recognition and Graphics.

Computing Milieux.

Computer and Information Systems Applications.

Machine Learning.

Physical Description:

1 online resource (IX, 194 pages) : 68 illustrations, 51 illustrations in color.

Edition:

1st ed. 2022.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2022.

System Details:

text file PDF

Summary:

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.

Contents:

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

The Learn2Reg 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. .

Other Format:

Printed edition:

ISBN:

978-3-030-97281-3

9783030972813

Access Restriction:

Restricted for use by site license.