皮肤癌临床特征数据集
基于智能手机图像和临床特征的皮肤癌多分类数据集，涵盖六类病变。
基本信息
资源简介
皮肤癌临床特征数据集
【一句话概览】
本数据集是 figshare 公开的多模态数据集，收录 1 个文件、合计 104.18 KB，主题涵盖 squamous cell carcinoma、possess considerable potential、new cases identified、especially deep learning、enhancing diagnostic sensitivity，可用于跨模态对齐与融合建模研究，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Clinical features.》。源站首次发布：2025-07-28。
【数据内容】
源站原始描述：Skin cancer is a significant global public health issue, with millions of new cases identified each year. Recent breakthroughs in artificial intelligence, especially deep learning, possess considerable potential to enhance the accuracy and efficiency of screening. This study proposes an approach that employs smartphone images, which are preprocessed using adaptive learning and Black-Hat transformation. ViT is utilized for feature extraction, and a stacking model is constructed employing these fe…
【数据结构与技术规格】
文件数 1 个，合计 104.18 KB。pone.0328402.s001.zip：application/zip，104.18 KB。数据集 DOI：10.1371/journal.pone.0328402.s001。
【主题与分类】
源站学科分类：Space Science、Medicine、Immunology、Biological Sciences not elsewhere classified、Cancer、Science Policy。主题标签：squamous cell carcinoma、possess considerable potential、new cases identified、especially deep learning、enhancing diagnostic sensitivity、basal cell carcinoma、suggested model attained、model &# 8217、like patient age、associated skin diseases。
【适用方向】
多模态数据适合跨模态对齐、融合建模与联合表征学习方向的研究。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/Clinical_features_/29657872
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Mertahi, Adil El; Ezzine, Hind; Douzi, Samira; Douzi, Khadija (2025). Clinical features.. PLOS ONE. Dataset
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（Figshare）
- 源站标题：Clinical features.
- DOI：10.1371/journal.pone.0328402.s001
- 发布日期：2025-07-28
- 文件数：1 个
- 文件总体积：104.18 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Clinical_features_/29657872
命令行下载
curl -L -o "pone.0328402.s001.zip" "https://ndownloader.figshare.com/files/56611153"
文件清单
pone.0328402.s001.zip（104.18 KB）
文件总体积 104.18 KB（源站 API 实测）。
数据说明
Skin cancer is a significant global public health issue, with millions of new cases identified each year. Recent breakthroughs in artificial intelligence, especially deep learning, possess considerable potential to enhance the accuracy and efficiency of screening. This study proposes an approach that employs smartphone images, which are preprocessed using adaptive learning and Black-Hat transformation. ViT is utilized for feature extraction, and a stacking model is constructed employing these features in conjunction with image-related variables, like patient age and sex, for final classification. The model’s efficacy in identifying cancer-associated skin diseases was evaluated across six categories of skin lesions: actinic keratosis, basal cell carcinoma, melanoma, nevus, squamous cell carcinoma, and seborrheic keratosis. The suggested model attained an overall accuracy of 97.61%, with a PVV of 96.88%, a recall of 97.63%, and an F1 score of 97.19%, so illustrating its efficacy in detecting malignant skin lesions. This method could greatly aid dermatologists by enhancing diagnostic sensitivity and specificity, reducing delays in identifying the most suspicious lesions, and ultimately reaching more patients in need of timely screenings and patient care, thus saving lives.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0328402.s001（source: https://plos.figshare.com/articles/dataset/Clinical_features_/29657872）。
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。
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