  Detecting and classifying cells in histopathology H\&E stained whole-slide
images is a core task in computational pathology, as it provides valuable
insight into the tumor microenvironment. In this work we investigate the impact
of ground truth formats on the models performance. Additionally, cell-tissue
interactions are considered by providing tissue segmentation predictions as
input to the cell detection model. We find that a "soft", probability-map
instance segmentation ground truth leads to best model performance. Combined
with cell-tissue interaction and test-time augmentation our Soft
Cell-Tissue-Model (SoftCTM) achieves 0.7172 mean F1-Score on the Overlapped
Cell On Tissue (OCELOT) test set, achieving the third best overall score in the
OCELOT 2023 Challenge. The source code for our approach is made publicly
available at https://github.com/lely475/ocelot23algo.
