Abstract is missing.
- Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization ChallengeFrauke Wilm, Christian Marzahl, Katharina Breininger, Marc Aubreville. 5-13 [doi]
- Assessing Domain Adaptation Techniques for Mitosis Detection in Multi-scanner Breast Cancer Histopathology ImagesJack Breen, Kieran Zucker, Nicolas M. Orsi, Nishant Ravikumar. 14-22 [doi]
- Domain-Robust Mitotic Figure Detection with Style TransferYoujin Chung, Jihoon Cho, Jinah Park. 23-31 [doi]
- Two-Step Domain Adaptation for Mitotic Cell Detection in Histopathology ImagesRamin Nateghi, Fattaneh Pourakpour. 32-39 [doi]
- Domain-Specific Cycle-GAN Augmentation Improves Domain Generalizability for Mitosis DetectionRutger H. J. Fick, Alireza Moshayedi, Gauthier Roy, Jules Dedieu, Stéphanie Petit, Saima Ben Hadj. 40-47 [doi]
- Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization ChallengeMostafa Jahanifar, Adam Shepard, Neda Zamanitajeddin, Raja Muhammad Saad Bashir, Mohsin Bilal, Syed Ali Khurram, Fayyaz A. Minhas, Nasir M. Rajpoot. 48-52 [doi]
- MitoDet: Simple and Robust Mitosis DetectionJakob Dexl, Michaela Benz, Volker Bruns, Petr Kuritcyn, Thomas Wittenberg. 53-57 [doi]
- Multi-source Domain Adaptation Using Gradient Reversal Layer for Mitotic Cell DetectionSatoshi Kondo. 58-61 [doi]
- Rotation Invariance and Extensive Data Augmentation: A Strategy for the MItosis DOmain Generalization (MIDOG) ChallengeMaxime W. Lafarge, Viktor H. Koelzer. 62-67 [doi]
- Detecting Mitosis Against Domain Shift Using a Fused Detector and Deep Ensemble Classification Model for MIDOG ChallengeJingtang Liang, Cheng Wang, Yujie Cheng, Zheng Wang, Fang Wang, Liyu Huang, Zhibin Yu, Yubo Wang. 68-72 [doi]
- Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) ChallengeXi Long, Ying Cheng, Xiao Mu, Lian Liu, Jingxin Liu. 73-76 [doi]
- Domain Generalisation for Mitosis Detection Exploting Preprocessing HomogenizersSahar Almahfouz Nasser, Nikhil Cherian Kurian, Amit Sethi. 77-80 [doi]
- Cascade R-CNN for MIDOG ChallengeSalar Razavi, Fariba Dambandkhameneh, Dimitri Androutsos, Susan Done, April Khademi. 81-85 [doi]
- Sk-Unet Model with Fourier Domain for Mitosis DetectionSen Yang, Feng Luo, Jun Zhang, Xiyue Wang. 86-90 [doi]
- Self-supervised 3D Out-of-Distribution Detection via Pseudoanomaly GenerationJihoon Cho, Inha Kang, Jinah Park. 95-103 [doi]
- Self-supervised Medical Out-of-Distribution Using U-Net Vision TransformersSeongjin Park, Adam Balint, Hyejin Hwang. 104-110 [doi]
- SS3D: Unsupervised Out-of-Distribution Detection and Localization for Medical VolumesLars Doorenbos, Raphael Sznitman, Pablo Márquez-Neila. 111-118 [doi]
- MetaDetector: Detecting Outliers by Learning to Learn from Self-supervisionJeremy Tan, Turkay Kart, Benjamin Hou, James Batten, Bernhard Kainz. 119-126 [doi]
- AutoSeg - Steering the Inductive Biases for Automatic Pathology SegmentationFelix Meissen, Georgios Kaissis, Daniel Rueckert. 127-135 [doi]
- Deformable Registration of Brain MR Images via a Hybrid LossLuyi Han, Haoran Dou, Yunzhi Huang, Pew-Thian Yap. 141-146 [doi]
- Fraunhofer MEVIS Image Registration Solutions for the Learn2Reg 2021 ChallengeAlessa Hering, Annkristin Lange, Stefan Heldmann, Stephanie Häger, Sven Kuckertz. 147-152 [doi]
- Unsupervised Volumetric Displacement Fields Using Cost Function UnrollingGal Lifshitz, Dan Raviv. 153-160 [doi]
- Conditional Deep Laplacian Pyramid Image Registration Network in Learn2Reg ChallengeTony C. W. Mok, Albert C. S. Chung. 161-167 [doi]
- The Learn2Reg 2021 MICCAI Grand Challenge (PIMed Team)Wei Shao 0008, Sulaiman Vesal, David S. Lim, Cynthia Li, Negar Golestani, Ahmed Alsinan, Richard E. Fan, Geoffrey A. Sonn, Mirabela Rusu. 168-173 [doi]
- Fast 3D Registration with Accurate Optimisation and Little Learning for Learn2Reg 2021Hanna Siebert, Lasse Hansen, Mattias P. Heinrich. 174-179 [doi]
- Progressive and Coarse-to-Fine Network for Medical Image Registration Across Phases, Modalities and PatientsSheng Wang 0011, Jinxin Lv, Hongkuan Shi, Yilang Wang, Yuanhuai Liang, Zihui Ouyang, Zhiwei Wang, Qiang Li. 180-185 [doi]
- Semi-supervised Multilevel Symmetric Image Registration Method for Magnetic Resonance Whole Brain ImagesMarek Wodzinski. 186-191 [doi]