威斯康星乳腺癌诊断数据集
该数据集包含从数字化乳腺细针穿刺图像中提取的细胞核特征数据，数据模态为经过计算的图像特征，主要用于乳腺癌的良恶性分类研究，支持机器学习模型在医学影像分析和癌症诊断领域的应用开发。
基本信息
资源简介
威斯康星乳腺癌诊断数据集
【数据集背景】
本数据集源站标题为《MohammadErfanRashidi/Breast-Cancer》。源站首次发布：2024-11-10。
【数据内容】
源站原始描述：This project demonstrates the use of Logistic Regression for predicting breast cancer based on the Wisconsin Breast Cancer Diagnostic dataset. # Breast Cancer Prediction using Logistic Regression This project demonstrates the use of Logistic Regression for predicting breast cancer based on the Wisconsin Breast Cancer Diagnostic dataset. ## Dataset The dataset used in this project is the Wisconsin Breast Cancer Diagnostic dataset, which contains features computed from a digitized image of a fine…
【获取与许可】
源站页面：https://github.com/MohammadErfanRashidi/Breast-Cancer
使用许可：MIT。
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取
git clone https://github.com/MohammadErfanRashidi/Breast-Cancer.git
curl -L -o repo.zip https://github.com/MohammadErfanRashidi/Breast-Cancer/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录（使用方式）
Breast Cancer Prediction using Logistic Regression
This project demonstrates the use of Logistic Regression for predicting breast cancer based on the Wisconsin Breast Cancer Diagnostic dataset.
Dataset
The dataset used in this project is the Wisconsin Breast Cancer Diagnostic dataset, which contains features computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. These features describe characteristics of the cell nuclei present in the image.
The dataset is available in the file data.csv. It includes the following columns:
- id: ID number
- diagnosis: Diagnosis (M = malignant, B = benign)
- radius_mean: Mean of distances from center to points on the perimeter
- texture_mean: Standard deviation of gray-scale values
- perimeter_mean: Mean size of the core tumor
- area_mean: Mean area of the core tumor
- smoothness_mean: Mean of local variation in radius lengths
- compactness_mean: Mean of perimeter^2 / area - 1.0
- concavity_mean: Mean of severity of concave portions of the contour
- concave points_mean: Mean for number of concave portions of the contour
- symmetry_mean: Mean symmetry
- fractal_dimension_mean: Mean “coastline approximation” - 1
- radius_se: Standard error for the mean of distances from center to points on the perimeter
- texture_se: Standard error for standard deviation of gray-scale values
- perimeter_se: Standard error for mean size of the core tumor
- area_se: Standard error for mean area of the core tumor
- smoothness_se: Standard error for local variation in radius lengths
- compactness_se: Standard error for perimeter^2 / area - 1.0
- concavity_se: Standard error for severity of concave portions of the contour
- concave points_se: Standard error for number of concave portions of the contour
- symmetry_se: Standard error for symmetry
- fractal_dimension_se: Standard error for “coastline approximation” - 1
- radius_worst: “Worst”
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。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
⚡️ 需要数据支持或标注服务？立即联系我们获取专业方案。
