2020/04/28 by Vinícius Gadis Ribeiro, Vinicius Ribeiro, Ribeiro, Vinicius +4 · 1 citation
Computer Science · Medicine · #Computer Vision and Pattern Recognition (cs.CV) #Cutaneous Melanoma Detection and Management #FOS: Computer and information sciences #Genital Health and Disease #Nonmelanoma Skin Cancer Studies #cs.CV
paper · pdf · doi:10.48550/arxiv.2004.13856
Accepted to the ISIC Skin Image Analysis Workshop @ CVPR 2020
arxiv created 2020/04/28 · openalex publication_date 2020/04/28 · arxiv updated 2020/04/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Segmenting skin lesions images is relevant both for itself and for assisting in lesion classification, but suffers from the challenge in obtaining annotated data. In this work, we show that segmentation may improve with less data, by selecting the training samples with best inter-annotator agreement, and conditioning the ground-truth masks to remove excessive detail. We perform an exhaustive experimental design considering several sources of variation, including three different test sets, two different deep-learning architectures, and several replications, for a total of 540 experimental runs. We found that sample selection and detail removal may have impacts corresponding, respectively, to 12% and 16% of the one obtained by picking a better deep-learning model.