A Breast Cancer Histology and Immunohistochemistry Image Dataset with Linked Clinicopathological Metadata

Abstract

Digital pathology and computational analysis of breast cancer require datasets that connect microscopic images with clinically meaningful diagnostic, morphological and immunohistochemical information. But images are often provided separately from pathomorphological reports, tumour profiles, biomarker assessments and segmentation masks, which limits their use for interpretable machine learning. This data descriptor presents a structured dataset of de-identified breast cancer cases that integrates H&E images, immunohistochemical images for ER, PR, HER2/neu and Ki-67, PNG cell segmentation masks and JSON-based clinical and morphological metadata. The dataset was formed from archival histological material, digitised microscopic fields of view and expert-verified diagnostic information. Each JSON record links the patient-level description, tumour profile, staining type, image file, mask file and marker-specific assessment, including Allred score fields for relevant immunohistochemical images. The proposed structure provides traceability from the diagnostic conclusion to individual image fields and associated masks. The dataset is intended to support research on tumour classification, cell segmentation, immunohistochemical marker quantification, HER2-related image analysis, Allred score modelling and explainable artificial intelligence in computational pathology.