Less is More: Sample Selection and Label Conditioning Improve Skin Lesion Segmentation

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Authors Eduardo Valle, Sandra Avila, Vinicius Ribeiro
Journal/Conference Name IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Paper Category
Paper Abstract 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.
Date of publication 2020
Code Programming Language Python

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