A Digital Pathology Resource for Liver Cancer Quantification with Datasets, Benchmarks, and Tools
This work provides a much-needed resource and tool for automated regional quantification in liver cancer pathology, enabling reproducible model development and fair method comparison for researchers and clinicians.
The authors released HepatoBench, a patch-level liver cancer image database with annotations for seven tissue categories, and built HepatoQuant, an end-to-end tool integrating a patch-level classifier and WSI-level segmentation for automated tissue quantification. They open-sourced the dataset, models, and benchmarks to address the scarcity of fine-grained annotated resources in liver cancer pathology.
Liver cancer, especially hepatocellular carcinoma (HCC), imposes a substantial global disease burden. Accurate diagnosis and prognostic assessment directly influence treatment selection and patient survival, and pathological examination remains the gold standard for liver cancer diagnosis. Identifying diverse tissue components and pathological subtypes on histopathology slides is crucial for estimating postoperative recurrence risk and overall prognosis. However, most publicly available resources are still provided at the whole-slide image (WSI) level, and well-annotated datasets for fine-grained tissue component identification in liver cancer are scarce, which hinders reproducible model development and the deployment of quantitative analysis tools. To address this gap, we release HepatoBench, a patch-level image database for liver cancer with annotations for seven key tissue categories. Based on HepatoBench, we train and open-source a deep learning classification model as a tissue recognition tool. Furthermore, we train a WSI-level tumor/non-tumor segmentation model to automatically localize lesion regions across entire slides. By integrating the patch-level tissue classifier with the WSI-level segmentation model, we build HepatoQuant, an end-to-end, disease-specific regional quantification tool for liver cancer, enabling a unified workflow from WSIs to tissue composition parsing and quantitative statistics. We also open-source HepatoBench, the benchmarking protocol, and supporting tools, providing a solid foundation for automated regional quantification and fair method comparison in liver cancer pathology.