PRECISE: Benchmarking digital pathology with expert-annotated contiguous IHC–H&E serial prostate sections

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 10.5281/zenodo.20721779.

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Publication Details

Journal
The Journal of Machine Learning for Biomedical Imaging
Published
2026-09-21
DOI
https://doi.org/10.59275/j.melba.2026-g657
Primary Topic
AI in cancer detection
Type
article
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article

PRECISE: Benchmarking digital pathology with expert-annotated contiguous IHC–H&E serial prostate sections

Miriam Cobo Cano, Adriana K. Calapaquí Terán, Sara Marcos González, Abel A. González Bernad et al.
The Journal of Machine Learning for Biomedical Imaging
AI in cancer detection
article

PRECISE: Benchmarking digital pathology with expert-annotated contiguous IHC–H&E serial prostate sections

Miriam Cobo Cano, Adriana K. Calapaquí Terán, Sara Marcos González, Abel A. González Bernad, Jaled Moustafá Calvo, Lucía Sánchez Magdaleno, José Javier Gómez Román, Roberto Carlos Delgado Bolton, Lara Lloret Iglesias
article en

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 10.5281/zenodo.20721779.

The Journal of Machine Learning for Biomedical ImagingVol. 2026(MICCAI Open Data 2026)
Universidad de Cantabria (ES), Marqués de Valdecilla University Hospital (ES), Acorde (Spain) (ES), Instituto de Investigación Marqués de Valdecilla (ES), Servicio Cántabro de Salud (ES), Instituto de Física de Cantabria (ES)
Openalex Percentile: Top 9%
AI in cancer detection
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