Busca avançada
Ano de início
Entree


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

Texto completo
Autor(es):
Ribeiro, Vinicius ; Avila, Sandra ; Valle, Eduardo ; IEEE COMP SOC
Número total de Autores: 4
Tipo de documento: Artigo Científico
Fonte: 2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW 2020); v. N/A, p. 10-pg., 2020-01-01.
Resumo

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. (AU)

Processo FAPESP: 17/16246-0 - Análise de mídias sensíveis usando arquiteturas de aprendizado profundo
Beneficiário:Sandra Eliza Fontes de Avila
Modalidade de apoio: Auxílio à Pesquisa - Regular
Processo FAPESP: 19/05018-1 - Triagem Automática de Doenças para o Mundo Real
Beneficiário:Eduardo Alves Do Valle Junior
Modalidade de apoio: Bolsas no Exterior - Pesquisa