We are presenting a first and still preliminary schedule of the MIDOG challenge workshop. Congratulation to all accepted presenters! Please Note that all times are UTC! Links to the
Block 1: Introduction and Reference Approach, 14:00-15:20 UTC (16:00 – 17:20 CEST)
| 14:00-14:20 | Welcome address and Introduction | Marc Aubreville |
| 14:20-15:05 | Keynote: How to build trustworthy AI solutions With the rise of Deep Learning technology and free availability of the required toolchains, developing machine learning (AI) algorithms has become more accessible than ever. In addition, performance of these algorithms is getting better and better, often straight out of the box. Is this sufficient to trust these AI systems enough for deployment in critical application areas as healthcare? What needs to be done to move from an academic proof-of-concept to a validated AI system? | Tobias Heimann |
| 15:05-15:20 | Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization (MIDOG) Challenge | Frauke Wilm |
Block 2: Oral Session 1: Cascaded and Multi-stage approaches for mitosis detection
15:30-16:05 UTC (17:30 – 18:05 CEST)
| 15:30-15:35 | Cascade RCNN for MIDOG Challenge | Salar Razavi |
| 15:35-15:40 | Domain Adaptive Cascade R-CNN for Mitosis DOmain Generalization (MIDOG) Challenge | Ying Cheng |
| 15:40-15:45 | Detecting Mitosis against Domain Shift using a Fused Detector and Deep Ensemble Classification Model for MIDOG Challenge | Yubo Wang |
| 15:45-15:50 | Two-step Domain Adaptation for Mitosis Cell Detection in Histopathology Images | Ramin Nateghi |
| 15:50-16:05 | Q&A and Discussion | all |
Block 3: Oral Session 2: Instance segmentation-based and adversarial approaches
16:05-16:40 UTC (18:05 – 18:40 CEST)
| 16:05-16:10 | Multi-source Domain Adaptation Using Gradient Reversal Layer for Mitotic Cell Detection | Satoshi Kondo |
| 16:10-16:15 | Sk-Unet Model with Fourier Domain for Mitosis Detection | Sen Yang |
| 16:15-16:20 | Robust Mitosis Detection Using a Cascade Mask-RCNN Approach With Domain-Specific Residual Cycle-GAN Data Augmentation | Saima Ben Hadj |
| 16:20-16:25 | Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge | Mostafa Jahanifar |
| 16:25-16:40 | Q&A and Discussion | all |
Block 4: Oral Session 3: Augmentation strategies for domain invariance
16:40-17:15 UTC (18:40 – 19:15 CEST)
| 16:40-16:45 | Rotation Invariance and Extensive Data Augmentation: a strategy for the Mitosis Domain Generalization (MIDOG) Challenge | Maxime Lafarge |
| 16:45-16:50 | Assessing domain adaptation techniques for mitosis detection in multi-scanner breast cancer histopathology images | Jack Breen |
| 16:50-16:55 | Domain-Robust Mitotic Figure Detection with StyleGAN | Youjin Chung |
| 16:55-17:00 | MitoDet: Simple and robust mitosis detection | Jakob Dexl |
| 17:00-17:15 | Q&A and Discussion | all |
Block 5: Results and Awards, Panel Discussion
17:25-18:00 UTC (19:25-20:00 CEST)
| 17:25-17:40 | Results and Awards | Marc Aubreville, Katharina Breininger |
| 17:40-18:00 | Panel Discussion | The MIDOG Organizers |