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      Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)

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          Abstract

          The International Skin Imaging Collaboration (ISIC) is a global partnership that has organized the world's largest public repository of dermoscopic images of skin lesions. This archive has been used for 3 consecutive years to host challenges on skin lesion analysis toward melanoma detection, covering 3 analysis tasks of lesion segmentation, lesion attribute detection, and disease classification. The most recent instance in 2018 was hosted at the Medical Image Computing and Computer Assisted Intervention (MICCAI) conference in Granada, Spain. The dataset included over 10,000 images. Approximately 900 users registered for data download, 115 submitted to the lesion segmentation task, 25 submitted to the lesion attribute detection task, and 159 submitted to the disease classification task, making this the largest study in the field to date. Important new analyses were introduced to better reflect the difficulties of translating research systems to clinical practice. This article summarizes the results of these analyses, and makes recommendations for future challenges in medical imaging.

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

          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.
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            Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)

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              Deep learning ensembles for melanoma recognition in dermoscopy images

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                Author and article information

                Journal
                08 February 2019
                Article
                1902.03368

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                https://challenge2018.isic-archive.com/
                cs.CV

                Computer vision & Pattern recognition

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