临床环境下基于深度学习诊断多种皮肤疾病的数据集

数据集获取（Figshare）

• 源站标题：Table_1_A Deep Learning Based Framework for Diagnosing Multiple Skin Diseases in a Clinical Environment.docx
• DOI：10.3389/fmed.2021.626369.s001
• 发布日期：2021-04-16
• 文件数：1 个
• 文件总体积：17.89 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_A_Deep_Learning_Based_Framework_for_Diagnosing_Multiple_Skin_Diseases_in_a_Clinical_Environment_docx/14429678

命令行下载

curl -L -o "Table_1_A Deep Learning Based Framework for Diagnosing Multiple Skin Diseases in a Clinical Environment.docx" "https://ndownloader.figshare.com/files/27582899"

文件清单

• Table_1_A Deep Learning Based Framework for Diagnosing Multiple Skin Diseases in a Clinical Environment.docx（17.89 KB）

文件总体积 17.89 KB（源站 API 实测）。

数据说明

Background: Numerous studies have attempted to apply artificial intelligence (AI) in the dermatological field, mainly on the classification and segmentation of various dermatoses. However, researches under real clinical settings are scarce.
Objectives: This study was aimed to construct a novel framework based on deep learning trained by a dataset that represented the real clinical environment in a tertiary class hospital in China, for better adaptation of the AI application in clinical practice among Asian patients.
Methods: Our dataset was composed of 13,603 dermatologist-labeled dermoscopic images, containing 14 categories of diseases, namely lichen planus (LP), rosacea (Rosa), viral warts (VW), acne vulgaris (AV), keloid and hypertrophic scar (KAHS), eczema and dermatitis (EAD), dermatofibroma (DF), seborrheic dermatitis (SD), seborrheic keratosis (SK), melanocytic nevus (MN), hemangioma (Hem), psoriasis (Pso), port wine stain (PWS), and basal cell carcinoma (BCC). In this study, we applied Google’‘s EfficientNet-b4 with pre-trained weights on ImageNet as the backbone of our CNN architecture. The final fully-connected classification layer was replaced with 14 output neurons. We added seven auxiliary classifiers to each of the intermediate layer groups. The modified model was retrained with our dataset and implemented using Pytorch. We constructed saliency maps to visualize our network’'s attention area of input images for its prediction. To explore the visual characteristics of different clinical classes, we also examined the internal image features learned by the proposed framework using t-SNE (t-distributed Stochastic Neighbor Embedding).
Results: Test results showed that the proposed framework achieved a high level of classification performance with an overall accuracy of 0.948, a sensitivity of 0.934 and a specificity of 0.950. We also compared the performance of our algorithm with three most widely used CNN models which showed our model outperformed existing models with the highest area under curve (AUC) of 0.985. We further compared this model with 280 board-certificated dermatologists, and results showed a comparable performance level in an 8-class diagn

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2021.626369.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_A_Deep_Learning_Based_Framework_for_Diagnosing_Multiple_Skin_Diseases_in_a_Clinical_Environment_docx/14429678）。