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ORCIDMIDOG/MOOD/Learn2Reg@MICCAI 2021: Strasbourg, France
- Marc Aubreville
, David Zimmerer, Mattias P. Heinrich:
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 13166, Springer 2022, ISBN 978-3-030-97280-6
MIDOG
- Frauke Wilm, Christian Marzahl, Katharina Breininger, Marc Aubreville:
Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization Challenge. 5-13 - Jack Breen, Kieran Zucker, Nicolas M. Orsi, Nishant Ravikumar:
Assessing Domain Adaptation Techniques for Mitosis Detection in Multi-scanner Breast Cancer Histopathology Images. 14-22 - Youjin Chung, Jihoon Cho, Jinah Park:
Domain-Robust Mitotic Figure Detection with Style Transfer. 23-31 - Ramin Nateghi, Fattaneh Pourakpour:
Two-Step Domain Adaptation for Mitotic Cell Detection in Histopathology Images. 32-39 - Rutger H. J. Fick, Alireza Moshayedi, Gauthier Roy, Jules Dedieu, Stéphanie Petit, Saima Ben Hadj:
Domain-Specific Cycle-GAN Augmentation Improves Domain Generalizability for Mitosis Detection. 40-47 - Mostafa Jahanifar, Adam J. Shephard, Neda Zamanitajeddin, Raja Muhammad Saad Bashir, Mohsin Bilal, Syed Ali Khurram, Fayyaz A. Minhas, Nasir M. Rajpoot:
Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge. 48-52 - Jakob Dexl, Michaela Benz, Volker Bruns, Petr Kuritcyn, Thomas Wittenberg:
MitoDet: Simple and Robust Mitosis Detection. 53-57 - Satoshi Kondo:
Multi-source Domain Adaptation Using Gradient Reversal Layer for Mitotic Cell Detection. 58-61 - Maxime W. Lafarge, Viktor H. Koelzer:
Rotation Invariance and Extensive Data Augmentation: A Strategy for the MItosis DOmain Generalization (MIDOG) Challenge. 62-67 - Jingtang Liang, Cheng Wang, Yujie Cheng, Zheng Wang, Fang Wang, Liyu Huang, Zhibin Yu, Yubo Wang:
Detecting Mitosis Against Domain Shift Using a Fused Detector and Deep Ensemble Classification Model for MIDOG Challenge. 68-72 - Xi Long, Ying Cheng, Xiao Mu, Lian Liu, Jingxin Liu:
Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge. 73-76 - Sahar Almahfouz Nasser, Nikhil Cherian Kurian, Amit Sethi:
Domain Generalisation for Mitosis Detection Exploting Preprocessing Homogenizers. 77-80 - Salar Razavi, Fariba Dambandkhameneh, Dimitri Androutsos, Susan Done, April Khademi:
Cascade R-CNN for MIDOG Challenge. 81-85 - Sen Yang, Feng Luo, Jun Zhang, Xiyue Wang:
Sk-Unet Model with Fourier Domain for Mitosis Detection. 86-90
MOOD
- Jihoon Cho, Inha Kang, Jinah Park:
Self-supervised 3D Out-of-Distribution Detection via Pseudoanomaly Generation. 95-103 - Seongjin Park, Adam Balint, Hyejin Hwang:
Self-supervised Medical Out-of-Distribution Using U-Net Vision Transformers. 104-110 - Lars Doorenbos, Raphael Sznitman, Pablo Márquez-Neila:
SS3D: Unsupervised Out-of-Distribution Detection and Localization for Medical Volumes. 111-118 - Jeremy Tan, Turkay Kart, Benjamin Hou, James Batten, Bernhard Kainz:
MetaDetector: Detecting Outliers by Learning to Learn from Self-supervision. 119-126 - Felix Meissen, Georgios Kaissis, Daniel Rueckert:
AutoSeg - Steering the Inductive Biases for Automatic Pathology Segmentation. 127-135
L2R
- Luyi Han, Haoran Dou, Yunzhi Huang, Pew-Thian Yap:
Deformable Registration of Brain MR Images via a Hybrid Loss. 141-146 - Alessa Hering, Annkristin Lange, Stefan Heldmann, Stephanie Häger, Sven Kuckertz:
Fraunhofer MEVIS Image Registration Solutions for the Learn2Reg 2021 Challenge. 147-152 - Gal Lifshitz, Dan Raviv:
Unsupervised Volumetric Displacement Fields Using Cost Function Unrolling. 153-160 - Tony C. W. Mok, Albert C. S. Chung:
Conditional Deep Laplacian Pyramid Image Registration Network in Learn2Reg Challenge. 161-167 - Wei Shao, Sulaiman Vesal, David S. Lim, Cynthia Xinran Li, Negar Golestani, Ahmed Alsinan, Richard E. Fan, Geoffrey A. Sonn, Mirabela Rusu:
The Learn2Reg 2021 MICCAI Grand Challenge (PIMed Team). 168-173 - Hanna Siebert, Lasse Hansen, Mattias P. Heinrich:
Fast 3D Registration with Accurate Optimisation and Little Learning for Learn2Reg 2021. 174-179 - Sheng Wang, Jinxin Lv, Hongkuan Shi, Yilang Wang, Yuanhuai Liang, Zihui Ouyang, Zhiwei Wang, Qiang Li:
Progressive and Coarse-to-Fine Network for Medical Image Registration Across Phases, Modalities and Patients. 180-185 - Marek Wodzinski:
Semi-supervised Multilevel Symmetric Image Registration Method for Magnetic Resonance Whole Brain Images. 186-191
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