结肠癌高光谱图像数据集
包含27张结肠腺癌肝转移患者的高光谱图像，用于术中肿瘤像素级分类，由三位病理学家标注。
基本信息
资源简介
结肠癌高光谱图像数据集
【数据集背景】
本数据集源站标题为《ikopriva/ColonCancerHSI》。源站首次发布：2024-11-22。
【数据内容】
源站原始描述：MATLAB scripts for semi-supervised pixel-wise classification of hyperspectral images of adenocarcinoma of the colon in the liver This directory contains three MATLAB scripts: MPRI\_HSI\_pseudoRGB_SSL.m, TensorSSA\_HSI\_pseudoRGB_SSL.m and Grassmann\_HSI.m. The first two scripts combine self-trained semi-supervised learning (SSL) algorithm, implemented through MATLAB function fitsemiself, with 3D spectral-spatial features extracted by: (1) Multiscale Priniciple of Relevant Information (MPRI) appr…
【适用方向】
影像类数据可用于医学图像分析模型（检测、分割、分类）的训练与评测，也可用于标注质量与成像参数相关性的研究。
【获取与许可】
源站页面：https://github.com/ikopriva/ColonCancerHSI
【使用注意】
· 源站最后修改于 2025-03-20，引用时请注意版本时效。
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/ikopriva/ColonCancerHSI.git
curl -L -o repo.zip https://github.com/ikopriva/ColonCancerHSI/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录（使用方式）
This directory contains three MATLAB scripts: MPRI_HSI_pseudoRGB_SSL.m, TensorSSA_HSI_pseudoRGB_SSL.m and Grassmann_HSI.m. The first two scripts combine self-trained semi-supervised learning (SSL) algorithm, implemented through MATLAB function fitsemiself, with 3D spectral-spatial features extracted by:
(1) Multiscale Priniciple of Relevant Information (MPRI) approach, Y. Wei, S. YU, L. S. Giraldo, J. C. Principe, “Multiscale principle of relevant information for hyperspectral image classification”, Machine Learning (2023) 112:1227-1252, https://doi.org/10.1007/s10994-021-06011-9.
Corresponding code is placed in sub-directory .\MPRI_code.
(2) Tensor Singular Spectrum Analysis (TensorSSA) approach, H. Fu, G. Sun, A. Zhang, B. Shao, J. Ren, and X. Jia, “Tensor Singular Spectrum Analysis for 3-D Feature Extraction in Hyperspectral Images,” IEEE Transactions of Geoscience and Remote Sensing", vol. 61, article no. 5403914, 2023.
Corresponding code is placed in sub-directory .\TensorSSA_code.
The third script, Grassmann_HSI.m, implements Grassmann-manifold and nearest subspace classifier with TensorSSA spectral-spatial features.
The scripts read either 27 hyperspectral images of 27 corresponding co-registered RGB color images assumed to be placed, together with ground truth files, in the same working directory. Consequently, prior to running the scripts data have to be downloaded from the repository: https://data.fulir.irb.hr/islandora/object/irb:538
This work is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
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。
