A patient-centric dataset of images and metadata for identifying melanomas using clinical context

By V. Rotemberg, N. Kurtansky, B. Betz‐Stablein, L. Caffery, E. Chousakos, Noel C. F. Codella, M. Combalia, S. Dusza, Pascale Guitera, David Gutman and 15 more. Scientific Data, 2020

562 citations recorded by the source corpus.

Prior skin image datasets have not addressed patient-level information obtained from multiple skin lesions from the same patient. Though artificial intelligence classification algorithms have achieved expert-level performance in controlled studies examining single images, in practice dermatologists base their judgment holistically from multiple lesions on the same patient. The 2020 SIIM-ISIC Melanoma Classification challenge dataset described herein was constructed to address this discrepancy between prior challenges and clinical practice, providing for each image in the dataset an identifier allowing lesions from the same patient to be mapped to one another. This patient-level contextual information is frequently used by clinicians to diagnose melanoma and is especially useful in ruling out false positives in patients with many atypical nevi. The dataset represents 2,056 patients (20.8% with at least one melanoma, 79.2% with zero melanomas) from three continents with an average of 16 lesions per patient, consisting of 33,126 dermoscopic images and 584 (1.8%) histopathologically confirmed melanomas compared with benign melanoma mimickers. Measurement(s) melanoma • Skin Lesion Technology Type(s) Dermoscopy • digital curation Factor Type(s) approximate age • sex • anatomic site Sample Characteristic - Organism Homo sapiens Measurement(s) melanoma • Skin Lesion Technology Type(s) Dermoscopy • digital curation Factor Type(s) approximate age • sex • anatomic site Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13070345

Subfields: Artificial Intelligence, Epidemiology, Oncology

View Paper: https://www.semanticscholar.org/paper/2ffcf95e4916d3a44c474f7a6cccaabe9d11df83

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Citations in the corpus

Post-hoc Concept Bottleneck Models (2022, methodology): “(Rotemberg et al., 2021) To test a real-world transfer learning use case, we evaluate the model trained on HAM10000 on a subset of the SIIM-ISIC Melanoma Classification dataset.”
Characteristics of publicly available skin cancer image datasets: a systematic review. (2021, background): “42,44,47 However, selection bias can occur where specific groups are excluded (eg, facial images or identifiable tattoos).”
Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization (2021, methodology): “For skin lesion segmentation, we report the per-patient JC index, following the ISIC challenge [79].”
Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset (2021, methodology): “With the exception of PAD-UFES-20 [42], none of the publicly available data sets identified by the Sixth ISIC Skin Image Analysis Workshop at CVPR 2021 (Derm7pt [30], Dermofit Image Library, ISIC 2018 [16, 52], ISIC 2019 [15, 52, 18], ISIC 2020[46, 17], MED-NODE [27], PH2 [37], SD-128 [49], SD-198, ”
BCN20000: Dermoscopic Lesions in the Wild (2019, background): “A significant catalyst for these algorithms’ adoption has been The International Skin Imaging Collaboration (ISIC), organizing annual challenges since 2016 for developing computer vision algorithms to classify and segment dermoscopic images of skin lesions 14–18 .”