SlideInstruction 和 SlideBench
包含最大WSI指令跟随数据集和病理图像多模态基准，用于训练和评估病理图像对话模型。
基本信息
资源简介
SlideInstruction 和 SlideBench
【数据集背景】
本数据集源站标题为《uni-medical/SlideChat》。源站首次发布：2025-03-19。
【数据内容】
源站原始描述：SlideChat: A Large Vision-Language Assistant for Whole-Slide Pathology Image Understanding # SlideChat: A Multimodal Generative AI Assistant for Whole-Slide Computational Pathology > **Note** > > This repository provides the official code for SlideChat. The expanded study, published in *Nature Cancer*, substantially extends our original CVPR 2025 work with a larger instruction dataset, broader expert-reviewed evaluation, and additional pathology tasks across cancer types. # Release We release **…
【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/uni-medical/SlideChat
使用许可：Apache-2.0。
【使用注意】
· 源站最后修改于 2026-09-02，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/uni-medical/SlideChat.git
curl -L -o repo.zip https://github.com/uni-medical/SlideChat/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录（使用方式）
SlideChat: A Multimodal Generative AI Assistant for Whole-Slide Computational Pathology
Note
This repository provides the official code for SlideChat. The expanded study, published in Nature Cancer, substantially extends our original CVPR 2025 work with a larger instruction dataset, broader expert-reviewed evaluation, and additional pathology tasks across cancer types.
Release
We release SlideChat, SlideInstruction, and SlideBench as open-source resources, hoping to facilitate research and development in computational pathology.
- SlideChat: The first large vision-language assistant for whole-slide pathology image analysis, capable of generating comprehensive descriptions and contextually relevant responses.
- SlideInstruction: The largest comprehensive WSI instruction-following dataset, derived from pathology reports…
- SlideBench: A WSI multimodal benchmark including SlideBench-Caption/Report (TCGA, CPTAC, HISTAI) and SlideBench-Closed(VQA-TCGA, VQA-BCNB, VQA-CPTAC, VQA-HISTAI).
Before open‑sourcing, SlideBench‑VQA‑TCGA underwent a second round of expert review with pathologists, further enhancing data quality. The initial version covers 10 cancer types with 1,494 samples. We later expanded it to 31 additional cancer types, rigorously validated by experts, yielding 3,176 samples (SlideBench‑VQA‑TCGA.csv). SlideChat achieved an accuracy of 75.23% on the initial version and 74.12% on the expanded version.
The results below correspond to the expanded journal version of SlideChat.
Closed-Ended Question Answering
| Dataset | Task | SlideChat | GPT-4o | MedDr | Quilt-LLaVA | LLaVA-Med |
|---|---|---|---|---|---|---|
| TCGA | Histopathological Changes | 0.859 ± 0.019 | 0.481 ± 0.027 | 0.533 ± 0.027 | 0.311 ± 0.025 | 0.292 ± 0.025 |
| TCGA | Cytomorphological Characteristics | 0.843 ± 0.040 | 0.584 ± 0.052 | 0.690 ± 0.050 | 0.180 ± 0.042 | 0.216 ± 0.045 |
| TCGA | Tissue Architecture | 0.836 ± 0.019 | 0.614 ± 0.026 | 0.601 ± 0.025 | 0.385 ± 0.026 | 0.465 ± 0.026 |
| TCGA | Tumor Characteristics | 0.717 ± 0.035 | 0.607 ± 0.039 | 0.612 ± 0.039 | 0.259 ± 0.034 | 0.306 ± 0.035 |
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。
