子宫内膜疾病组织病理图像数据集

数据集获取（Figshare）

• 源站标题：A histopathological image dataset for endometrial disease diagnosis
• DOI：10.6084/m9.figshare.7306361.v2
• 发布日期：2018-11-07
• 文件数：2 个
• 文件总体积：207.33 MB
• 源站页面：https://figshare.com/articles/dataset/A_histopathological_image_dataset_for_endometrial_disease_diagnosis/7306361

命令行下载

curl -L -o "histopathological image dataset for ET.zip" "https://ndownloader.figshare.com/files/13496366"
curl -L -o "Readme.txt" "https://ndownloader.figshare.com/files/13496387"

文件清单

• histopathological image dataset for ET.zip（207.33 MB）
• Readme.txt（862 B）

文件总体积 207.33 MB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Recent
studies have shown that computer-aided diagnosis (CADx) systems based on deep learning
can perform even better than human experts in detecting some common diseases,
such as diabetic retinopathy and pneumonia. However, the scarcity of labeled
medical imaging data impedes a broad application of this technique to diagnosis of other malignant tumors that receive
relatively little attention, especially for women. For example, uterine cancer
(also known as endometrial cancer) can seriously affect the female reproductive
organs, but it receives less attention than breast cancer and lung cancer. We developed
a histopathological image dataset of the endometrium to help alleviate the shortage
of pathologically proven cases in using deep learning algorithms to train classification
models for endometrial diseases. The dataset comprises ~3,300 joint-photographic-experts-group
(JPEG) images from ~500 endometrial specimens stained with hematoxylin and
eosin (H&E), each of which has a unique class label derived from the
corresponding diagnosis made by three expert pathologists collectively. Note
that we will continue to enrich this dataset in the future. All the related source code is
available at https://github.com/ssea-lab/DL4ETI.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.7306361.v2（source: https://figshare.com/articles/dataset/A_histopathological_image_dataset_for_endometrial_disease_diagnosis/7306361）。