Program

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