PRECISE: Benchmarking digital pathology with expert-annotated contiguous IHC–H&E serial prostate sections
Adriana K. Calapaquı́ Terán1,2,3, Abel A. González Bernad3,4, Miriam Cobo Cano5, Lucı́a Sánchez Magdaleno1, Sara Marcos González1,2,3, Roberto Carlos Delgado Bolton2, Jaled Moustafá Calvo4, José Javier Gómez Román1,2,3,6, Lara Lloret Iglesias5
1: Department of Pathology, University Hospital “Marqués de Valdecilla”, Santander, Spain, 2: Servicio Cántabro de Salud, Santander, Spain, 3: Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain, 4: Siali Technologies, SL, Santander, Spain, 5: Grupo de Computación Avanzada y e-Ciencia, Instituto de Fı́sica de Cantabria (UC-CSIC), Santander, Spain, 6: Universidad de Cantabria, Santander, Spain
Publication date: 2026/09/21
https://doi.org/10.59275/j.melba.2026-g657
Abstract
We present PRECISE (PRostate Expert-annotated Contiguous IHC–H&E Serial sEctions), a hybrid histopathology dataset of paired hematoxylin and eosin (H&E) and immunohistochemistry (IHC) whole-slide images (WSIs), comprising 37 prostate core needle biopsies from 25 patients, each with matched H&E and CKAPM+racemase staining. To the best of our knowledge, this is the first publicly available dataset offering spatially harmonized, pixel-level expert annotations across both staining modalities in prostate biopsy WSIs — directly mirroring the two-stage (H&E-then-IHC) clinical diagnostic workflow used to resolve morphological uncertainty, restricted to cases in which that workflow reached diagnostic consensus. The dataset contains 24,387 annotations spanning seven diagnostically critical classes: malignant glands, benign glands, stromal tissue, intraductal carcinoma (IDC-P), high-grade prostatic intraepithelial neoplasia (HGPIN), atypical intraductal proliferation (AIP), and tissue artifacts. Unlike existing resources, which focus on binary tumor classification or lack IHC pairing, this dataset captures the full morphological spectrum encountered in routine prostate pathology, including rare precursor lesions and confounding entities underrepresented in current benchmarks. Annotations were validated through a structured three-stage consensus by two expert uropathologists, with IHC serving as biological ground truth for boundary definition. PRECISE is designed as a robust benchmark for multimodal semantic segmentation and self-supervised learning, and is openly released to promote reproducible research and accelerate AI-assisted diagnosis in prostate cancer. Our dataset is available at