Task-Ready PanNuke and NuCLS Datasets: Reorganization, Synthetic Data Generation, and Experimental Evaluation

Automating nuclei analysis in histopathology is essential for enhancing disease diagnosis; however, training reliable models necessitates well-structured datasets. This paper addresses the gap in standardized data preparation workflows for two critical histopathology datasets: PanNuke and NuCLS. First, we organize histopathology images and masks into training-validation splits, extract subsets specific to cell types, and generate multi-scale patches to enable robust model training across various resolutions using the PanNuke dataset. Second, we curate task-specific subsets for object detection and semantic segmentation, ensuring consistency across splits while addressing annotation inconsistencies with the NuCLS dataset. Third, we conduct experiments on two reorganized datasets, including cell-type-specific binary classification, multi-task evaluation, and extension to synthetic datasets. Our workflows address common histopathology data challenges, including fragmented annotations, class imbalance, and mismatched metadata. The processed datasets are shared in standardized formats, allowing researchers to train models directly for critical tasks such as detecting cancerous nuclei or segmenting inflammatory cells in histopathology images.