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Test-Time Selection for Robust Skin Lesion Analysis

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Autor(es):
Bissoto, Alceu ; Barata, Catarina ; Valle, Eduardo ; Avila, Sandra
Número total de Autores: 4
Tipo de documento: Artigo Científico
Fonte: MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2023 WORKSHOPS; v. 14393, p. 10-pg., 2023-01-01.
Resumo

Skin lesion analysis models are biased by artifacts placed during image acquisition, which influence model predictions despite carrying no clinical information. Solutions that address this problem by regularizing models to prevent learning those spurious features achieve only partial success, and existing test-time debiasing techniques are inappropriate for skin lesion analysis due to either making unrealistic assumptions on the distribution of test data or requiring laborious annotation from medical practitioners. We propose TTS (Test-Time Selection), a human-in-the-loop method that leverages positive (e.g., lesion area) and negative (e.g., artifacts) keypoints in test samples. TTS effectively steers models away from exploiting spurious artifact-related correlations without retraining, and with less annotation requirements. Our solution is robust to a varying availability of annotations, and different levels of bias. We showcase on the ISIC2019 dataset (for which we release a subset of annotated images) how our model could be deployed in the real-world for mitigating bias. (AU)

Processo FAPESP: 13/08293-7 - CECC - Centro de Engenharia e Ciências Computacionais
Beneficiário:Munir Salomao Skaf
Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
Processo FAPESP: 19/19619-7 - Geração ilimitada de imagens de lesões de pele usando redes generativas adversariais
Beneficiário:Alceu Emanuel Bissoto
Modalidade de apoio: Bolsas no Brasil - Doutorado
Processo FAPESP: 22/09606-8 - Compreensão do papel de atalhos e mudanças de distribuição para generalização de redes neurais profundas
Beneficiário:Alceu Emanuel Bissoto
Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Doutorado
Processo FAPESP: 20/09838-0 - BI0S - Brazilian Institute of Data Science
Beneficiário:João Marcos Travassos Romano
Modalidade de apoio: Auxílio à Pesquisa - Programa Centros de Pesquisa em Engenharia