Dense Prediction of Cell Centroids Using Tissue Context and Cell Refinement

Cell detection is a common task in computational pathology, often fundamental for downstream tasks that can aid in predicting prognosis or treatment response. The Overlapped Cell on Tissue Dataset for Histopathology (OCELOT) challenge aimed to explore ways to improve automated cell detection algorithms by leveraging surrounding tissue information. We developed two cell detection algorithms for this challenge that both leverage surrounding tissue context to enhance their performance. The first is fed an additional input representing a cancer area probability heatmap, predicted from a tissue segmentation model. The second is fed the cancer area probability heatmap, in addition to a heatmap representing cell locations predicted from a separate model. Submitting our first algorithm, we achieved a mean F1 score of 74.73 on the challenge validation set, and second place with a mean F1 score of 72.21 on the challenge test set. Our algorithms do not require paired cell and tissue annotations to train, enabling their use to enhance existing cell detection models where paired annotations may not exist.