| 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 |