多模态伤口分类数据集
Multi-modal Wound Classification Dataset 是与瘢痕（Scars）相关的公开数据资源，主要包含伤口ROI图像、解剖部位信息和多类别创面标签，数据模态为多模态，适合用于分类、模型训练、基准评测或临床特征分析。
基本信息
资源简介
多模态伤口分类数据集包含伤口ROI图像、解剖部位信息和多类别创面标签，可用于瘢痕相关的分类研究。
下载信息
注册下载
Tips: 该数据集需要在对应的数据源网站注册通过后，才能进行数据下载，注册有对应要求，或者需要收费。
暂未开放公开下载
Tips: 该数据集属于公开下载，应该可以免费公开下载。
免登录有偿下载
Tips: 该数据集 Qianfanghub 可以协助提供有偿下载服务，注意，服务不针对数据相关产权，只是技术服务费。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放千方医数集，医疗数据集部分，是为社区服务的公开医疗数据集搜索引擎，并不存储或者下载原始的任何数据。 如果您有其他医疗数据需求，可以和客服联系，或者下工单。我们有强大的三甲医疗机构帮助您提供个性化的医疗数据定制、采集、标注服务。
使用方式
数据集获取
git clone https://github.com/uwm-bigdata/Multi-modal-wound-classification-using-images-and-locations.git
curl -L -o repo.zip https://github.com/uwm-bigdata/Multi-modal-wound-classification-using-images-and-locations/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录（使用方式）
Call for Papers: Special Issue on <ins>Recent Advances in Artificial Intelligence for Wound Assessment</ins>
In collaboration with Bioengineering, an open access journal by MDPI, we are soliciting papers in various AI technologies for wound assessment. More detail can be found below or at the journal’'s webpage.
Wound classification using images and locations
Dataset information: AZH dataset is collected over a two-year clinical period at the AZH Wound and Vascular Center in Milwaukee, Wisconsin. The dataset includes 730 wound images in .jpg format. The images are of various sizes, where the width ranging from 320 to 700 pixels and the height ranging from 240 to 525 pixels. These images contain four different wound types: venous, diabetic, pressure, and surgical. iPad Pro (software version 13.4.1) and a Canon SX 620 HS digital camera are used to capture the images, and labeling is done by a wound specialist from the AZH Wound and Vascular Center. For most images in our dataset, each image is taken from a separate patient. But there are a few cases where multiple photos were taken from the same patient at different body sites or various healing stages. For the latter case, the wound shapes are different, so they are considered separate images.
Pre-Processing:
- The wound ROIs are cropped using our developed localizer (https://ieeexplore.ieee.org/abstract/document/9785640).
- Wound professionals annotate the corresponding ROI locations with detailed location information using our custom body map (Download BodyMap.zip).
Please read the following articles for more details.
Publication
- Patel, Y., Shah, T., Mrinal Kanti Dhar, Zhang, T., Niezgoda, J., Gopalakrishnan, S., & Yu, Z. (2024). Integrated image and location analysis for wound classification: a deep learning approach. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-56626-w
- Anisuzzaman, D. M., Patel, Y., Rostami, B., Niezgoda, J., Gopalakrishnan, S., & Yu, Z. (2022). Multi-modal wound classification using wound image and location by deep neural network. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-21813-0
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。
完整仓库：github.com/uwm-bigdata/Multi-modal-wound-classification-using-images-and-locations
精度瓶颈？数据缺失？
当前公开数据无法满足您的算法精度？千方提供针对 瘢痕 的高质量、多模态真实临床数据定制解决方案。
