Citation

Rehman, Zaka Ur and Ahmad Fauzi, Mohammad Faizal and Wan Ahmad, Wan Siti Halimatul Munirah and Abas, Fazly Salleh and Cheah, Phaik Leng and Chiew, Seow Fan and Looi, Lai Meng (2025) HER2-SISH40x: Annotated histopathology image dataset for HER2 amplification assessment in breast cancer. Data in Brief, 63. p. 111941. ISSN 2352-3409

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Abstract

The HER2 overexpression serves as a crucial biomarker in breast cancer diagnosis and treatment decision-making. In-situ hybridization (ISH) is widely employed for determination HER2 gene amplification. We introduce the HER2-SISH40x dataset based on VENTANA HER2 Dual probe ISH (DISH) staining, consisting of image patches curated from 50 whole slide images (WSIs) acquired at 40 × magnification using a 3DHistech Pannoramic DESK scanner. Expert pathologists annotated 237 regions of interest (ROIs) categorized as breast cancer with HER2 amplification (HER2/CEP17 >2 and HER2 signals/cancer cell >4), or breast cancer without HER2 amplification (HER2/CEP17 <2 and HER2 signals/cancer cell <4), with an additional 300 Normal ROIs extracted from both Amplified and Non-Amplified WSIs. This dataset is suitable for developing and evaluating computational pathology methods, particularly deep learning models for automated HER2 scoring, segmentation, and classification. The dataset has been successfully applied in several research studies [1-3], addressing challenges such as color normalization, cancer-region classification, and automated HER2 signal quantification. Research ethical clearance was obtained from the University Malaya Medical Center. The HER2-SISH40x dataset offers a valuable resource for advancing digital pathology workflows and personalized breast cancer diagnosis.

Item Type:	Article
Uncontrolled Keywords:	Amplified regions, Breast cancer, Human epidermal growth factor receptor 2 (HER2), Silver-enhanced in situ hybridization (SISH)
Subjects:	R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Divisions:	Faculty of Engineering and Technology (FET) Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Nor Afiqah Mohd Adnan
Date Deposited:	07 Nov 2025 06:50
Last Modified:	09 Nov 2025 22:20
URII:	http://shdl.mmu.edu.my/id/eprint/14776

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