ArticleScientific data2021
A patient-centric dataset of images and metadata for identifying melanomas using clinical context.
Article in Scientific data, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 106 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
106 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence for the Classification of Pigmented Skin Lesions in Populations with Skin of Color: A Systematic Review.Dermatology (Basel, Switzerland) · 2023Pooled it
- Neurophysiological Characterization of ADHD in Children Using EEG Signals: A Machine Learning Approach to Executive Function Networks.Sensors (Basel, Switzerland) · 2026Article
- Concept inconsistency in dermoscopic concept bottleneck models: a rough-set analysis of the Derm7pt dataset.Scientific reports · 2026Article
- Ultraviolet Sensing-Guided Biomedical Systems: From Label-Free Imaging to Dosimetry and Therapy Feedback.Biosensors · 2026Review
- Towards generalizable AI in medicine via Generalist-Specialist Collaboration.Nature biomedical engineering · 2026Article
- Automated Melanocytic Lesion Classification: Capsule Networks Trained With Synthetic Images Can Outperform Networks Trained With Real Images.The Australasian journal of dermatology · 2026Article
- Multimodal models for skin cancer classification using clinical freetext and dermatoscopic images.Communications medicine · 2026Article
- Advanced hybrid transformer CNN framework for improved skin lesion classification and segmentation.Scientific reports · 2026Article
- Multimodal artificial intelligence for enhanced skin cancer diagnosis and prognosis.Discover oncology · 2026Review
- Synthetic Melanoma Image Generation and Evaluation Using Generative Adversarial Networks.Bioengineering (Basel, Switzerland) · 2026Article
- DermIDS: Dermatology imaging data structure for scalable and interoperable AI systems.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Multispectral imaging and autofluorescence photobleaching combined in a multi-head neural network for skin cancer classification.Frontiers in medicine · 2026Article
- A Deep Metric Learning and Multimodal Gated Fusion Framework for AI-Driven Risk Assessment of Lingual Plate Perforation and Mandibular Canal Injury in Posterior Mandible Implants.International journal of dentistry · 2026Article
- HyperFusionNet combines vision transformer for early melanoma detection and precise lesion segmentation.Scientific reports · 2025Article
- 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
- Out-of-Distribution Detection as a Risk-Control Strategy for Medical Classification Machine Learning Models.Clinical and translational science · 2025Article
- A longitudinal dataset of tile and corresponding dermoscopic images with metadata for identifying skin cancers.Scientific data · 2025Article
- Semi-supervised GAN with hybrid regularization and evolutionary hyperparameter tuning for accurate melanoma detection.Scientific reports · 2025Article
46 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
25 authors.
Funding
Abstract
Prior skin image datasets have not addressed patient-level information obtained from multiple skin lesions from the same patient. Though artificial intelligence classification algorithms have achieved expert-level performance in controlled studies examining single images, in practice dermatologists base their judgment holistically from multiple lesions on the same patient. The 2020 SIIM-ISIC Melanoma Classification challenge dataset described herein was constructed to address this discrepancy between prior challenges and clinical practice, providing for each image in the dataset an identifier allowing lesions from the same patient to be mapped to one another. This patient-level contextual information is frequently used by clinicians to diagnose melanoma and is especially useful in ruling out false positives in patients with many atypical nevi. The dataset represents 2,056 patients (20.8% with at least one melanoma, 79.2% with zero melanomas) from three continents with an average of 16 lesions per patient, consisting of 33,126 dermoscopic images and 584 (1.8%) histopathologically confirmed melanomas compared with benign melanoma mimickers.
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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.