Test-Time Selection for Robust Skin Lesion Analysis

Autor(es)

Bissoto, Alceu ; Barata, Catarina ; Valle, Eduardo ; Avila, Sandra

• 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 Aplicada