Current projects

Annotation software

We are involved in two annotation frameworks for microscopy images:

  • EXACT is a web-based collaboration toolset for algorithm-aided annotation of images with annotation version control [paper] [github]
  • SlideRunner: A PyQT-based open source tool for massive cell annotations. [paper] [github]

We strongly believe in the importance of democratizing data access in order to drive advancements in our research domain. We therefore made available the following data sets:

  • The CATCH dataset is a tumor tissue segmentation dataset, comprising of 350 whole slide images of seven different canine cutaneous tumors complemented by 12,424 polygon annotations for 13 histologic classes. [paper] [github]
  • The MITOS_WSI_CMC dataset is the currently largest dataset for breast cancer that is annotated on complete whole slide images. It contains 21 cases of canine breast cancer and was labeled as the consensus of three experts. [paper] [github]
  • The MITOS_WSI_CCMCT dataset is the largest dataset for mitosis on canine cutaneous mast cell tumor and the largest dataset with verified mitotic figures (around 44k cells). Each cell has been labeled as a consensus of three pathologists. [paper] [github]
  • The TUPAC16_AL dataset is a set of alternative labels for the TUPAC16 challenge auxiliary mitosis dataset. Our research paper shows that it’s more complete and has a lower level of label noise. [paper] [github]
  • The EIPH dataset is an Inter-species cell detection – dataset aimed at detecting pulmonary hemosiderophages in equine, human and feline specimens. [paper] [github]

Microscopy Machine Learning Approaches

Our mission is to not improve insignificantly over previous work but to define new and interesting approaches, leading to new insights in the field of microscopy and pathology. Some of our most recent work is:

  • Jonas Ammeling: Attention-based Multiple Instance Learning for Survival Prediction on Lung Cancer Tissue Microarrays [paper]
  • Jonathan Ganz: Deep Learning-based Automatic Assessment of AgNOR-scores in Histopathology Images [paper]
  • Marc Aubreville: Deep Learning-based Subtyping of Atypical and Normal Mitoses using a Hierarchical Anchor- free Object Detector [paper]
  • Frauke Wilm: Mind the Gap: Scanner-induced domain shifts pose challenges for representation learning in histopathology [paper]

We organized the Mitosis Domain Generalization Challenge (MIDOG) on MICCAI in the years 2021 and 2022. For the first round, you can find a summary of the challenge in our MEDIA paper:
Aubreville, M., Stathonikos, N., Bertram, C. A., Klopfleisch, R., Ter Hoeve, N., Ciompi, F., … & Breininger, K. (2023). Mitosis domain generalization in histopathology images—The MIDOG challenge. Medical Image Analysis84, 102699.

MIDOG 2022 Challenge:

MIDOG 2021 Challenge: