1 code implementation • 2 Feb 2024 • Miguel Correia, Alceu Bissoto, Carlos Santiago, Catarina Barata
Skin cancer detection through dermoscopy image analysis is a critical task.
1 code implementation • 14 Aug 2023 • Levy Chaves, Alceu Bissoto, Eduardo Valle, Sandra Avila
Transfer learning boosts the performance of medical image analysis by enabling deep learning (DL) on small datasets through the knowledge acquired from large ones.
1 code implementation • 10 Aug 2023 • Alceu Bissoto, Catarina Barata, Eduardo Valle, Sandra Avila
Skin lesion analysis models are biased by artifacts placed during image acquisition, which influence model predictions despite carrying no clinical information.
no code implementations • 9 May 2023 • Alceu Bissoto, Catarina Barata, Eduardo Valle, Sandra Avila
Our protocol reveals three findings: 1) Models learn and propagate correlation shifts even with low-bias training; this poses a risk of accumulating and combining unaccountable weak biases; 2) Models learn robust features in high- and low-bias scenarios but use spurious ones if test samples have them; this suggests that spurious correlations do not impair the learning of robust features; 3) Diversity shift can reduce the reliance on spurious correlations; this is counter intuitive since we expect biased models to depend more on biases when invariant features are missing.
1 code implementation • 20 Aug 2022 • Alceu Bissoto, Catarina Barata, Eduardo Valle, Sandra Avila
We propose a pipeline that relies on artifacts annotation to enable generalization evaluation and debiasing for the challenging skin lesion analysis context.
1 code implementation • 1 Jun 2022 • Zahra Mirikharaji, Kumar Abhishek, Alceu Bissoto, Catarina Barata, Sandra Avila, Eduardo Valle, M. Emre Celebi, Ghassan Hamarneh
We analyze these works along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules, and losses), and evaluation aspects (data annotation requirements and segmentation performance).
1 code implementation • 17 Jun 2021 • Levy Chaves, Alceu Bissoto, Eduardo Valle, Sandra Avila
Self-supervised pre-training appears as an advantageous alternative to supervised pre-trained for transfer learning.
1 code implementation • 20 Apr 2021 • Alceu Bissoto, Eduardo Valle, Sandra Avila
Despite the growing availability of high-quality public datasets, the lack of training samples is still one of the main challenges of deep-learning for skin lesion analysis.
1 code implementation • 23 Apr 2020 • Alceu Bissoto, Eduardo Valle, Sandra Avila
Data-driven models are now deployed in a plethora of real-world applications - including automated diagnosis - but models learned from data risk learning biases from that same data.
no code implementations • 29 Oct 2019 • Alceu Bissoto, Eduardo Valle, Sandra Avila
Generative Adversarial Networks fostered a newfound interest in generative models, resulting in a swelling wave of new works that new-coming researchers may find formidable to surf.
1 code implementation • 18 Apr 2019 • Alceu Bissoto, Michel Fornaciali, Eduardo Valle, Sandra Avila
We fed models with additional clinically meaningful information, which failed to improve the results even slightly, suggesting the destruction of cogent correlations.
2 code implementations • 8 Feb 2019 • Alceu Bissoto, Fábio Perez, Eduardo Valle, Sandra Avila
Skin cancer is by far the most common type of cancer.
no code implementations • 25 Aug 2018 • Alceu Bissoto, Fábio Perez, Vinícius Ribeiro, Michel Fornaciali, Sandra Avila, Eduardo Valle
This extended abstract describes the participation of RECOD Titans in parts 1 to 3 of the ISIC Challenge 2018 "Skin Lesion Analysis Towards Melanoma Detection" (MICCAI 2018).