ArticleScientific data2024
The SLICE-3D dataset: 400,000 skin lesion image crops extracted from 3D TBP for skin cancer detection.
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 20 papers.
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Who cites it
20 citing papers in PubMed.
- Research Progress on Construction Technology of 3D Human Skin Models and Its Application Prospects in Dermatology.International journal of molecular sciences · 2026Review
- DermIDS: Dermatology imaging data structure for scalable and interoperable AI systems.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- A model combining deep learning and ensemble learning for melanoma recognition via dermoscopy.Frontiers in oncology · 2026Article
- Targeting macrophage function with botulinum toxin: a novel translational strategy for preventing and treating pathological scars.Postepy dermatologii i alergologii · 2026Review
- Federated learning for privacy-preserving skin cancer classification using deep neural networks.Frontiers in oncology · 2026Article
- Artificial intelligence in three-dimensional total-body photography for skin cancer surveillance.Frontiers in medicine · 2026Review
- Explainable multimodal AI for skin lesion risk prediction via 3D imaging and clinical data.Scientific reports · 2025Article
- Artificial Intelligence and New Technologies in Melanoma Diagnosis: A Narrative Review.Cancers · 2025Review
- Automated triage of cancer-suspicious skin lesions with 3D total-body photography.NPJ digital medicine · 2025Article
- Improving dataset transparency in dermatologic Artificial Intelligence using a dataset nutrition label.NPJ digital medicine · 2025Article
- A methodology for developing dermatological datasets: lessons from retrospective data collection for AI-based applications.BMC medical research methodology · 2025Article
- A longitudinal dataset of tile and corresponding dermoscopic images with metadata for identifying skin cancers.Scientific data · 2025Article
- New Release of User-Captured Images from the Oregon Health & Science University Melanoma MoleMapper Project.Scientific data · 2025Article
- A multimodal vision foundation model for clinical dermatology.Nature medicine · 2025Article
- Automatic melanoma detection using an optimized five-stream convolutional neural network.Scientific reports · 2025Article
- SkinEHDLF a hybrid deep learning approach for accurate skin cancer classification in complex systems.Scientific reports · 2025Article
- AcuSim: A Synthetic Dataset for Cervicocranial Acupuncture Points Localisation.Scientific data · 2025Article
- Artificial Intelligence in the Non-Invasive Detection of Melanoma.Life (Basel, Switzerland) · 2024Review
- DERM12345: A Large, Multisource Dermatoscopic Skin Lesion Dataset with 40 Subclasses.Scientific data · 2024Article
- Dual-stage segmentation and classification framework for skin lesion analysis using deep neural network.Digital healthArticle
Corrections and comments
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Authors and funding
34 authors.
Funding
Abstract
AI image classification algorithms have shown promising results when applied to skin cancer detection. Most public skin cancer image datasets are comprised of dermoscopic photos and are limited by selection bias, lack of standardization, and lend themselves to development of algorithms that can only be used by skilled clinicians. The SLICE-3D ("Skin Lesion Image Crops Extracted from 3D TBP") dataset described here addresses those concerns and contains images of over 400,000 distinct skin lesions from seven dermatologic centers from around the world. De-identified images were systematically extracted from sensitive 3D Total Body Photographs and are comparable in optical resolution to smartphone images. Algorithms trained on lower quality images could improve clinical workflows and detect skin cancers earlier if deployed in primary care or non-clinical settings, where photos are captured by non-expert physicians or patients. Such a tool could prompt individuals to visit a specialized dermatologist. This dataset circumvents many inherent limitations of prior datasets and may be used to build upon previous applications of skin imaging for cancer detection.
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