ArticleScientific data2024
DERM12345: A Large, Multisource Dermatoscopic Skin Lesion Dataset with 40 Subclasses.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Mitigating hallucinations in synthesized clinical texts to improve multimodal deep learning for dermatology.Journal of biomedical informatics · 2026Article
- Concept inconsistency in dermoscopic concept bottleneck models: a rough-set analysis of the Derm7pt dataset.Scientific reports · 2026Article
- DermaCalibra: A Robust and Explainable Multimodal Framework for Skin Lesion Diagnosis via Bayesian Uncertainty and Dynamic Modulation.Diagnostics (Basel, Switzerland) · 2026Article
- Fine-Tuning a Small Vision Language Model Using Synthetic Data for Explaining Bacterial Skin Disease Images.Diagnostics (Basel, Switzerland) · 2026Article
- DermIDS: Dermatology imaging data structure for scalable and interoperable AI systems.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Improving Clinical Reliability of Diffusion-Based Dermatology Image Synthesis Through Synthetic Note Alignment and Post-Generation Filtering.IEEE International Conference on Omni-layer Intelligent Systems : COINS. IEEE International Conference on Omni-layer Intelligent Systems · 2026Article
- Artificial Intelligence and New Technologies in Melanoma Diagnosis: A Narrative Review.Cancers · 2025Review
- A methodology for developing dermatological datasets: lessons from retrospective data collection for AI-based applications.BMC medical research methodology · 2025Article
- Combining Real and Synthetic Data to Overcome Limited Training Datasets in Multimodal Learning.medRxiv : the preprint server for health sciences · 2025Article
- DERM12345: A Large, Multisource Dermatoscopic Skin Lesion Dataset with 40 Subclasses.Scientific data · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
Skin lesion datasets provide essential information for understanding various skin conditions and developing effective diagnostic tools. They aid the artificial intelligence-based early detection of skin cancer, facilitate treatment planning, and contribute to medical education and research. Published large datasets have partially coverage the subclassifications of the skin lesions. This limitation highlights the need for more expansive and varied datasets to reduce false predictions and help improve the failure analysis for skin lesions. This study presents a diverse dataset comprising 12,345 dermatoscopic images with 40 subclasses of skin lesions, collected in Turkiye, which comprises different skin types in the transition zone between Europe and Asia. Each subgroup contains high-resolution images and expert annotations, providing a strong and reliable basis for future research. The detailed analysis of each subgroup provided in this study facilitates targeted research endeavors and enhances the depth of understanding regarding the skin lesions. This dataset distinguishes itself through a diverse structure with its 5 super classes, 15 main classes, 40 subclasses and 12,345 high-resolution dermatoscopic images.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.