2023/12/19 by Lydia A. Schoenpflug, Viktor H. Koelzer, Schoenpflug, Lydia A. +1 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · #AI in cancer detection #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #Digital Imaging for Blood Diseases #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2312.12151
openalex publication_date 2023/12/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.