Investigating Imaging, Annotation and Self-Supervision for the Classification of Continuously Developing Cells in Histological Whole Slide Images

The analysis of individual cells is increasingly automated through deep learning techniques. This is particularly relevant for high-resolution whole slide images (WSIs), which can contain thousands of cells, making manual evaluation impractical. This increase in automation, however, requires higher levels of standardisation (with respect to the scanning hardware, settings and staining) and is further aggravated by the dynamics of the underlying cellular processes, rendering unique cell classifications difficult. To address these difficulties we investigated the entire processing pipeline (from imaging over annotation to model training) and study its underlying trade-offs. In particular, we created a new dataset comprising of more than 6, 300 labelled and 500, 000 unlabelled cells scanned using two different scan settings, resulting in fully registered image pairs with varying level of detail and quality. Using these alternative dataset versions we analysed the impact of inter- and intra-variability between three different annotators and addressed the challenge of limited labelled data by comparing the impact of different self-supervised pretraining strategies. Overall, our analyses provide new insights into the dependencies between imaging, annotation, self-supervision and deep learning-based classification, especially in the context of continuously developing cells and demonstrate the beneficial impact of these considerations on the overall classification accuracy. Code is available at https://zivgitlab.uni-muenster.de/cvmls/icdc and the data will be shared upon qualified request due to data privacy laws.