{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/the-ham10000-dataset-a-large-collection-of","title":"The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions","arxiv_id":"1803.10417","date":"2018-03-28","proceeding":null,"authors":["Philipp Tschandl","Cliff Rosendahl","Harald Kittler"],"abstract":"Training of neural networks for automated diagnosis of pigmented skin lesions\nis hampered by the small size and lack of diversity of available datasets of\ndermatoscopic images. We tackle this problem by releasing the HAM10000 (\"Human\nAgainst Machine with 10000 training images\") dataset. We collected\ndermatoscopic images from different populations acquired and stored by\ndifferent modalities. Given this diversity we had to apply different\nacquisition and cleaning methods and developed semi-automatic workflows\nutilizing specifically trained neural networks. The final dataset consists of\n10015 dermatoscopic images which are released as a training set for academic\nmachine learning purposes and are publicly available through the ISIC archive.\nThis benchmark dataset can be used for machine learning and for comparisons\nwith human experts. Cases include a representative collection of all important\ndiagnostic categories in the realm of pigmented lesions. More than 50% of\nlesions have been confirmed by pathology, while the ground truth for the rest\nof the cases was either follow-up, expert consensus, or confirmation by in-vivo\nconfocal microscopy.","url_abs":"http://arxiv.org/abs/1803.10417v3","url_pdf":"http://arxiv.org/pdf/1803.10417v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"the-ham10000-dataset-a-large-collection-of","repo_url":"https://github.com/ptschandl/HAM10000_dataset","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"caffe2","reach":{"status":"ok"}},{"paper_slug":"the-ham10000-dataset-a-large-collection-of","repo_url":"https://github.com/DinaSolitah/Skin-Disease-Analyzer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"the-ham10000-dataset-a-large-collection-of","repo_url":"https://github.com/MustafaAshraf348/Skin-Disease-Analyzer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"the-ham10000-dataset-a-large-collection-of","repo_url":"https://github.com/Woodman718/FixCaps","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"the-ham10000-dataset-a-large-collection-of","repo_url":"https://github.com/anitadala/SkinLesionAnalyzer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"the-ham10000-dataset-a-large-collection-of","repo_url":"https://github.com/armaanjawed/Skin-Cancer-Web_App","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"the-ham10000-dataset-a-large-collection-of","repo_url":"https://github.com/deepak-netizen/melanoma-detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"the-ham10000-dataset-a-large-collection-of","repo_url":"https://github.com/junaid54541/Skin-Cancer-Classification-Tflite-Model","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"the-ham10000-dataset-a-large-collection-of","repo_url":"https://github.com/romba050/Skin-Lesion-Analyzer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"the-ham10000-dataset-a-large-collection-of","repo_url":"https://github.com/shunk031/chainer-skin-lesion-detector","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"the-ham10000-dataset-a-large-collection-of","repo_url":"https://github.com/skrantidatta/Attention-based-Skin-Cancer-Classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"the-ham10000-dataset-a-large-collection-of","repo_url":"https://github.com/uyxela/Skin-Lesion-Classifier","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"the-ham10000-dataset-a-large-collection-of","repo_url":"https://github.com/vbookshelf/Skin-Lesion-Analyzer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"diagnostic","task_name":"Diagnostic"},{"task_slug":"diversity","task_name":"Diversity"}],"methods":[],"datasets_introduced":[{"slug":"ham10000-1","name":"HAM10000","full_name":"HAM10000"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1803.10417","atlas_url":"https://app.syntology.ai/?focus=1803.10417","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.10417"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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