特应性皮炎诊断多模态机器学习方法软件可用性数据集
特应性皮炎与非特应性皮炎研究样本的临床数据表格，用于多模态深度学习诊断模型验证。
基本信息
资源简介
特应性皮炎诊断多模态机器学习方法软件可用性数据集
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集，收录 1 个文件，主题涵盖 multimodal artificial intelligence、resnet50 model、diagnostic study、clinical decision support tool、Machine Learning, Explainable AI，可用于统计建模、特征工程与队列分析，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Training data image code_Software Availability Multimodal Machine Learning Approach for Diagnosing Atopic Dermatitis》。项目资助方：This study was supported by the Indonesian Education Scholarship, the Center for Higher Education Funding and Assessment, and the Indonesian Endowment Fund for Education. The authors would like to thank the board-certified dermatologists, participating hospitals, and patients for their contributions. We also thank our collaborators in data science and medical informatics.。源站首次发布：2025-08-18。
【数据内容】
源站原始描述：This research is part of our multi phase research for doctoral study program. This study presents a novel multimodal deep learning model that combines clinical image analysis with structured anamnesis using a late-fusion architecture. ResNet50 was employed for image-based feature extraction, while MPNet was used to process textual clinical data. Trained and validated across multiple centers, the model achieved an internal validation accuracy of 98.28%, significantly outperforming single-modality…
中文译文（机器翻译，仅供快速理解，请以原文为准）：本研究是我们博士研究项目多阶段研究的一部分。本研究提出了一种新型多模态深度学习模型，该模型采用晚期融合架构，将临床图像分析与结构化病史采集相结合。ResNet50用于基于图像的特征提取，MPNet用于处理文本临床数据。该模型在多个中心进行了训练和验证，内部验证准确率达到98.28%，显著优于单模态模型和标准诊断基线。我们的方法不仅提高了特应性皮炎的诊断准确性，还凸显了多模态AI在支持皮肤科临床决策方面的转化潜力。若这项研究能发表在Scopus收录的期刊上，我们将深感荣幸，并将其作为我们对促进航空安全的贡献。
【数据结构与技术规格】
文件数 1 个。数据集 DOI：10.6084/m9.figshare.29874716.v4。
【主题与分类】
源站学科分类：Artificial intelligence not elsewhere classified、Dermatology、Machine learning not elsewhere classified、Deep learning、Allergy、Immunology not elsewhere classified。主题标签：multimodal artificial intelligence、resnet50 model、diagnostic study、clinical decision support tool、Machine Learning, Explainable AI、immunology/allergy and hypersensitivity、atopic dermatitis。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://figshare.com/articles/dataset/Clinical_data_of_AD_and_non-AD_research_samples_from_phases_1_and_2_xlsx/29874716
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Widiawaty, Alida; Indriatmi, Wresti; Jatmiko, Wisnu; Novianto, Endi; Gunawan, Hendra; Kekalih, Aria; et al. (2025). Training data image code_Software Availability Multimodal Machine Learning Approach for Diagnosing Atopic Dermatitis. figshare. 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：<b>Training data image code</b><b>_</b><b>Software Availability </b><b>Multimodal Machine Learning Approach for Diagnosing Atopic Dermatitis</b>
- DOI：10.6084/m9.figshare.29874716.v4
- 发布日期：2025-08-18
- 文件数：1 个
- 源站页面：https://figshare.com/articles/dataset/Clinical_data_of_AD_and_non-AD_research_samples_from_phases_1_and_2_xlsx/29874716
命令行下载
curl -L -o "multimodal-skin-lesion-classification" "https://github.com/rxa15/multimodal-skin-lesion-classification"
文件清单
multimodal-skin-lesion-classification（大小未知）
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。
数据说明
This research is part of our multi phase research for doctoral study program. This study presents a novel multimodal deep learning model that combines clinical image analysis with structured anamnesis using a late-fusion architecture. ResNet50 was employed for image-based feature extraction, while MPNet was used to process textual clinical data. Trained and validated across multiple centers, the model achieved an internal validation accuracy of 98.28%, significantly outperforming single-modality models and standard diagnostic baselines. Our approach not only enhances diagnostic accuracy for atopic dermatitis but also highlights the translational potential of multimodal AI to support clinical decision-making in dermatology. It would be an honor for this research to be published in a Scopus-indexed journal, and we offer it as our contribution to promoting aviation safety.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.6084/m9.figshare.29874716.v4（source: https://figshare.com/articles/dataset/Clinical_data_of_AD_and_non-AD_research_samples_from_phases_1_and_2_xlsx/29874716）。
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。
