基于细胞学深度学习的原发灶不明肿瘤起源预测数据

数据集获取（Figshare）

• 源站标题：Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning
• DOI：10.6084/m9.figshare.25270066.v1
• 发布日期：2024-04-17
• 文件数：1 个
• 文件总体积：824.01 MB
• 源站页面：https://springernature.figshare.com/articles/dataset/Prediction_of_tumor_origin_in_cancers_of_unknown_primary_origin_with_cytology-based_deep_learning/25270066

命令行下载

curl -L -o "cytological images.rar" "https://ndownloader.figshare.com/files/44738662"

文件清单

• cytological images.rar（824.01 MB）

文件总体积 824.01 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

Cytological images including benign group, digestive system (consisting of both cavity digestive system and secretory digestive system), female reproductive system (including breast cancer), respiratory system and blood-lymphatic system

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.25270066.v1（source: https://springernature.figshare.com/articles/dataset/Prediction_of_tumor_origin_in_cancers_of_unknown_primary_origin_with_cytology-based_deep_learning/25270066）。