HAM10000 is a dataset of 10000 training images for detecting pigmented skin lesions. The authors collected dermatoscopic images from different populations, acquired and stored by different modalities.
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The MSK dataset is a dataset for lesion recognition from the Memorial Sloan-Kettering Cancer Center. It is used as part of the ISIC lesion recognition challenges.
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BCN_20000 is a dataset composed of 19,424 dermoscopic images of skin lesions captured from 2010 to 2016 in the facilities of the Hospital Clínic in Barcelona. The dataset can be used for lesion recognition tasks such as lesion segmentation, lesion detection and lesion classification.
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The goal for ISIC 2019 is classify dermoscopic images among nine different diagnostic categories.25,331 images are available for training across 8 different categories. Two tasks will be available for participation: 1) classify dermoscopic images without meta-data, and 2) classify images with additional available meta-data.
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The SD-198 dataset contains 198 different diseases from different types of eczema, acne and various cancerous conditions. There are 6,584 images in total. A subset include the classes with more than 20 image samples, namely SD-128."
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The dataset contains 33,126 dermoscopic training images of unique benign and malignant skin lesions from over 2,000 patients. Each image is associated with one of these individuals using a unique patient identifier. All malignant diagnoses have been confirmed via histopathology, and benign diagnoses have been confirmed using either expert agreement, longitudinal follow-up, or histopathology. A thorough publication describing all features of this dataset is available in the form of a pre-print that has not yet undergone peer review.
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The Mpox Close Skin Images dataset (MCSI) is a collection of skin images obtained from diverse public sources, that we accurately pre-processed (i.e., cropped and zoomed) in order to focus the skin lesion (if present), and to evaluate Machine Learning models aimed at detecting different pathologies from skin lesion pictures taken with smartphone cameras. It includes a total of 400 pictures homogeneously divided in 4 different classes: mpox, which contains samples of mpox (formerly Monkeypox) skin lesions; chickenpox, with samples of chickenpox cases; acne, containing samples of acne at different severity levels; and healthy, which contains samples of skin without any evident symptoms. This repository is part of the supplementary material accompanying the paper named: A Transfer Learning and Explainable Solution to Detect mpox from Smartphones images.
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