mlsanityData to 14 Oct 2026

The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions

arXiv 1803.10417 ↗First posted 28 Mar 2018cs.CVcited by 26

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Abstract (arXiv)

Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available datasets of dermatoscopic images. We tackle this problem by releasing the HAM10000 ("Human Against Machine with 10000 training images") dataset. We collected dermatoscopic images from different populations acquired and stored by different modalities. Given this diversity we had to apply different acquisition and cleaning methods and developed semi-automatic workflows utilizing specifically trained neural networks. The final dataset consists of 10015 dermatoscopic images which are released as a training set for academic machine learning purposes and are publicly available through the ISIC archive. This benchmark dataset can be used for machine learning and for comparisons with human experts. Cases include a representative collection of all important diagnostic categories in the realm of pigmented lesions. More than 50% of lesions have been confirmed by pathology, while the ground truth for the rest of the cases was either follow-up, expert consensus, or confirmation by in-vivo confocal microscopy.

Key facts. Cited by 26 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Mar 2018.

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Citing papers per year: 2023: 1; 2024: 8; 2025: 12; 2026: 5.

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  1. 2024CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Modelscited by 64
  2. 2025BIOMEDICA: An Open Biomedical Image-Caption Archive, Dataset, and Vision-Language Models Derived from Scientific Literaturecited by 34
  3. 2024MultiMedEval: A Benchmark and a Toolkit for Evaluating Medical Vision-Language Modelscited by 29
  4. 2024BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Modelscited by 28
  5. 2024Uni-Med: A Unified Medical Generalist Foundation Model For Multi-Task Learning Via Connector-MoEcited by 19
  6. 2024A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Modelscited by 16
  7. 2025MM-Skin: Enhancing Dermatology Vision-Language Model with an Image-Text Dataset Derived from Textbookscited by 14
  8. 2023Enhancing Visual Grounding and Generalization: A Multi-Task Cycle Training Approach for Vision-Language Modelscited by 8
  9. 2025Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentationcited by 7
  10. 2024MUCM-Net: A Mamba Powered UCM-Net for Skin Lesion Segmentationcited by 5
  11. 2025RL4Med-DDPO: Reinforcement Learning for Controlled Guidance Towards Diverse Medical Image Generation using Vision-Language Foundation Modelscited by 5
  12. 2025MambaVesselNet++: A Hybrid CNN-Mamba Architecture for Medical Image Segmentationcited by 5
14 more
  1. 2024Adapting Large Multimodal Models to Distribution Shifts: The Role of In-Context Learningcited by 4
  2. 2025Can Generalist Vision Language Models (VLMs) Rival Specialist Medical VLMs? Benchmarking and Strategic Insightscited by 4
  3. 2025UltraLBM-UNet: Ultralight Bidirectional Mamba-based Model for Skin Lesion Segmentationcited by 3
  4. 2025See-in-Pairs: Reference Image-Guided Comparative Vision-Language Models for Medical Diagnosiscited by 2
  5. 2025Prompting Medical Vision-Language Models to Mitigate Diagnosis Bias by Generating Realistic Dermoscopic Imagescited by 1
  6. 2025Intersectional Fairness in Vision-Language Models for Medical Image Disease Classificationcited by 1
  7. 2024Expert Routing with Synthetic Data for Continual Learningcited by 0
  8. 2025Prompt-Guided Dual-Path UNet with Mamba for Medical Image Segmentationcited by 0
  9. 2025Doctor Sun: A Bilingual Multimodal Large Language Model for Biomedical AIcited by 0
  10. 2026PhenoLIP: Integrating Phenotype Ontology Knowledge into Medical Vision-Language Pretrainingcited by 0
  11. 2026Geometry-Aware Distillation for Prompt Tuning Biomedical Vision-Language Modelscited by 0
  12. 2026S2COPE: Self-Supervised Concept Discovery via Preference Learningcited by 0
  13. 2026MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classificationcited by 0
  14. 2026Weakly supervised concept Bottleneck Learning for Robust Two stage Object centric visual reasoningcited by 0