PolypScene-250
包含250个息肉案例的多视角内镜图像数据集,用于结直肠癌可解释性诊断和检索。

基本信息
资源简介
PolypScene-250
【数据集背景】
本数据集源站标题为《ku262/EndoFinder-Scene》。源站首次发布:2025-06-17。
【数据内容】
源站原始描述:# EndoFinder: Online Lesion Retrieval for Explainable Colorectal Polyp Diagnosis Leveraging Latent Scene Representations A preliminary version of this work was published at MICCAI 2024. Code is available at here. This work significantly extends that work by moving from 2D image retrieval to a 2.5D/3D scene retrieval paradigm. We achieve this by explicitly modeling polyps as scenes and leveraging multi-view perspectives to capture implicit 3D information within a latent scene representation. ____…
【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/ku262/EndoFinder-Scene
【使用注意】
· 源站最后修改于 2026-08-20,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/ku262/EndoFinder-Scene.git
curl -L -o repo.zip https://github.com/ku262/EndoFinder-Scene/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录(使用方式)
EndoFinder: Online Lesion Retrieval for Explainable Colorectal Polyp Diagnosis Leveraging Latent Scene Representations
A preliminary version of this work was published at MICCAI 2024. Code is available at here.
This work significantly extends that work by moving from 2D image retrieval to a 2.5D/3D scene retrieval paradigm. We achieve this by explicitly modeling polyps as scenes and leveraging multi-view
perspectives to capture implicit 3D information within a latent
scene representation.
<div align=“center”>
Setup
-
Environment
An example of installation commands is provided as follows:
git clone git@github.com:ku262/EndoFinder-Scene.git cd EndoFinder-Scene pip3 install torch torchvision torchaudio pip3 install -r requirements.txt -
Model Preparation
We will release the trained models from our original experiments to
facilitate reproducibility. The release of EndoFinder-Scene.pth is planned upon acceptance of the paper. -
Data Preparation
We introduce PolypScene-250, a novel multi-view polyp dataset, including the PolypScene-80 subset with detailed histopathology annotations.
Sampling
-
Train
python train.py \ batch_size=512 \ epochs=200 \ output_dir=outputs \ finetune=EndoFinder.pth \ -
Val
python demo.py \ batch_size=64 \ resume=EndoFinder_Scene.pth \
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。




