The SLICE-3D dataset: 400,000 skin lesion image crops extracted from 3D TBP for skin cancer detection
Nicholas R. Kurtansky, B D'Alessandro, Maura C. Gillis, Brigid Betz‐Stablein, Sara E. Cerminara, Rafael Pérez García, Marcela Alves Girundi, Elisabeth Victoria Goessinger, Philippe Gottfrois, Pascale Guitera, Allan C. Halpern, Valerie Jakrot, Harald Kittler, Kıvanç Köse, Konstantinos Liopyris, Josep Malvehy, Victoria Mar, Linda K. Martin, Thomas Mathew, Lara Valeska Maul, Adam Mothershaw, Alina Miriam Mueller, Christoph Mueller, Alexander A. Navarini, Tarlia Rajeswaran, Vin Rajeswaran, Anup Saha, Maithili Sashindranath, Laura Serra‐García, H. Peter Soyer, Georgios Theocharis, Ayesha Vos, Jochen Weber, Veronica Rotemberg
Abstract
AI image classification algorithms have shown promising results when applied to skin cancer detection. Most public skin cancer image datasets are comprised of dermoscopic photos and are limited by selection bias, lack of standardization, and lend themselves to development of algorithms that can only be used by skilled clinicians. The SLICE-3D ("Skin Lesion Image Crops Extracted from 3D TBP") dataset described here addresses those concerns and contains images of over 400,000 distinct skin lesions from seven dermatologic centers from around the world. De-identified images were systematically extracted from sensitive 3D Total Body Photographs and are comparable in optical resolution to smartphone images. Algorithms trained on lower quality images could improve clinical workflows and detect skin cancers earlier if deployed in primary care or non-clinical settings, where photos are captured by non-expert physicians or patients. Such a tool could prompt individuals to visit a specialized dermatologist. This dataset circumvents many inherent limitations of prior datasets and may be used to build upon previous applications of skin imaging for cancer detection.