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A Comparison of Machine Learning Methods for the Diagnosis of Pigmented Skin Lesions.

, , , , , and . J. Biomed. Informatics, 34 (1): 28-36 (2001)

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Dermtrainer: A Decision Support System for Dermatological Diseases., , , , , , , , and . CoRR, (2019)Domain-specific classification-pretrained fully convolutional network encoders for skin lesion segmentation., , and . Comput. Biol. Medicine, (2019)Reproduction of patterns in melanocytic proliferations by agent-based simulation and geometric modeling., , , , , , and . PLoS Comput. Biol., (2021)The HAM10000 Dataset: A Large Collection of Multi-Source Dermatoscopic Images of Common Pigmented Skin Lesions., , and . CoRR, (2018)Detecting cutaneous basal cell carcinomas in ultra-high resolution and weakly labelled histopathological images., , , , , , , and . CoRR, (2019)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)., , , , , , , , , and 1 other author(s). CoRR, (2017)Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma., , , , , , , , , and 25 other author(s). CoRR, (2023)A patient-centric dataset of images and metadata for identifying melanomas using clinical context, , , , , , , , , and 15 other author(s). Scientific Data, (January 2021)An Application of Process Mining in the Context of Melanoma Surveillance Using Time Boxing., , , , and . Business Process Management Workshops, volume 342 of Lecture Notes in Business Information Processing, page 175-186. Springer, (2018)Classifying Pigmented Skin Lesions with Machine Learning Methods., , , and . ANNIMAB, page 174-179. Springer, (2000)