A PREPRINT
Marc Combalia1, Noel C. F. Codella2, Veronica Rotemberg3, Brian Helba4, Veronica Vilaplana5, Ofer Reiter3, Cristina
Carrera1, Alicia Barreiro1, Allan C. Halpern3, Susana Puig1, and Josep Malvehy1
ABSTRACT
This article summarizes the BCN20000 dataset, composed of 19424 dermoscopic images of skin
lesions captured from 2010 to 2016 in the facilities of the Hospital Clínic in Barcelona. With this
dataset, we aim to study the problem of unconstrained classification of dermoscopic images of skin
cancer, including lesions found in hard-to-diagnose locations (nails and mucosa), large lesions which
do not fit in the aperture of the dermoscopy device, and hypo-pigmented lesions. The BCN20000
will be provided to the participants of the ISIC Challenge 2019 [8], where they will be asked to train
algorithms to classify dermoscopic images of skin cancer automatically
Comprehensive Guidelines and Indications for Digital Monitoring of Patients at High-Risk for Melanoma: A Position Paper by the Israeli Association for Dermatology
Ofer Reiter, Mor Miodovnik, Emily Avitan-Hersh, Nadav Astman, Nir Nathanson, Ayelet Rishpon, Aviv Barzilai and Alon Scope Introduction: Early detection