Abstract
Accurate cell classification in histopathology images is critical for investigating biological mechanisms behind disease progression and discovering interpretable biomarkers for precision medicine. However, major challenges remain including a limited amount of annotated data and inconsistency in the annotated cell types across different datasets. Here, we combine two manually annotated datasets to curate a single large dataset consisting of approximately 206,000 nuclei images for five common cell types in the tumor microenvironment. We compare three deep learning models (ResNet18, ConvNeXt, and Efficient-net) trained with three different loss functions for cell classification. The best-performing model achieves an overall accuracy of 91.5% (range 63-98%) with an average one vs rest micro area under curve (AUC) of 0.98. This study provides a comprehensive evaluation of the state-of-the-art deep learning models using a benchmark dataset and demonstrates the need for further improvement for accurately classifying specific cell types.