HER2-IHC-40x: High-Resolution Histopathology Image Dataset for HER2 Scoring in Breast Cancer
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下载链接：
https://zenodo.org/record/15179607
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资源简介：
HER2-IHC-40x: High-Resolution Histopathology Datasets for HER2 IHC Scoring
Overview
This dataset contains high-resolution histopathological images of HER2-stained breast cancer tissue sections. Designed for deep learning-based HER2 scoring, the dataset includes two variants:
HER2-IHC-40x: Patches extracted after splitting WSIs.
HER2-IHC-40x-WSI: Patches extracted before splitting.
Each image patch is categorized into one of four HER2 classes (0, 1+, 2+, 3+), based on staining intensity.
Dataset Contents
Dataset Variants
1. HER2-IHC-40x
WSI-based 80-20 split before patch extraction.
107 WSIs → 9940 patches (8093 train / 1847 test)
2. HER2-IHC-40x-WSI
Patch-based 80-20 split after patch extraction.
107 WSIs → 10,997 patches (8897 train / 2200 test)
Folder Structure
HER2-IHC-40x/
├── WSI/ # Original Whole Slide Images (.svs)
├── ROI/ # Expert-annotated tumor regions (.png)
├── Patches/ # 1024x1024 image patches, labeled 0, 1+, 2+, 3+
├── Train/ # 80% training set
└── Test/ # 20% test set
HER2-IHC-40x-WSI/
├── Patches/
├── Train/
└── Test/
HER2 Class Definitions
| HER2 Score | Description
|----------|-----------------------------------------------------------------------------
| 0 | No observable staining
| 1+ | Weak/incomplete membrane staining in ≤10% tumor cells
| 2+ | Moderate circumferential staining in >10% tumor cells (Equivocal)
| 3+ | Strong circumferential staining in >10% tumor cells (Positive)
Preprocessing & Quality Control
ROI Selection: Manual annotation by expert pathologists using Cytomine.
Color Histogram Filtering: Removed non-tumor/low-quality patches using HSV filtering.
Normalization: Intensity normalization across all patches.
Patch Extraction: Adaptive 1024×1024 extraction using sliding window method.
Usage
This dataset is suitable for:
HER2 scoring automation using deep learning
Explainable AI (Grad-CAM, attention models)
Color normalization and domain adaptation
Model benchmarking and generalization research
Dataset Statistics
HER2-IHC-40x (WSI Split)
| HER2 Class | WSIs | ROIs | Patches |
|----------|------|----- -|---------|
| 0 | 23 | 429 | 3789 |
| 1+ | 26 | 131 | 2153 |
| 2+ | 27 | 483 | 634 |
| 3+ | 31 | 156 | 3364 |
| Total | 107 | 1199 | 9940 |
HER2-IHC-40x (Patch Split)
| HER2 Class | WSIs | ROIs | Patches |
|----------|-----|--------|---------|
| 0 | 23 | 429 | 3789 |
| 1+ | 26 | 131 | 2689 |
| 2+ | 27 | 483 | 1131 |
| 3+ | 31 | 156 | 3388 |
| Total | 107 | 1199 | 10,997 |
Citation
If you use this dataset, please cite:
```bibtex
@dataset{nabi2025her2,
author = {Md Serajun Nabi and Mohammad Faizal Ahmad Fauzi and Hezerul Bin Abdul Karim and Phaik Leng Cheah and Seow Fan Chiew and Lai Meng Looi},
title = {HER2-IHC-40x and HER2-IHC-40x-WSI: High-Resolution Histopathology Dataset for HER2 IHC Scoring in Breast Cancer},
year = 2025,
publisher = {Zenodo},
doi = {10.5281/zenodo.15179608},
url = {https://zenodo.org/record/xxxxxxx}
}
This dataset is part of the research article:
**"Enhancing HER2 IHC Scoring Using HRNet and SwinT with Cross-Dataset Generalization"**
Authors: Md Serajun Nabi, Mohammad Faizal Ahmad Fauzi, Hezerul Bin Abdul Karim, et al.
(Preprint server or journal details to be confirmed.)
Search the paper for detail data description:
**HER2-IHC-40x: High-Resolution Histopathology Datasets for HER2 IHC Scoring**
The color histogram code source:* https://github.com/seraju77/HER2-IHC-40x-data-preprocessing.git *
创建时间：
2025-04-09
