3 citations · 3 across the 3 of their papers we have counts for
3 papers
Fair Foundation Models for Medical Image Analysis: Challenges and Perspectives
Dilermando Queiroz, Anderson Carlos, André Anjos +1
Ensuring equitable Artificial Intelligence (AI) in healthcare demands systems that make unbiased decisions across all demographic groups, bridging technical innovation with ethical…
Does Data-Efficient Generalization Exacerbate Bias in Foundation Models?
Dilermando Queiroz, Anderson Carlos, Maíra Fatoretto +3
Foundation models have emerged as robust models with label efficiency in diverse domains. In medical imaging, these models contribute to the advancement of medical diagnoses due to…
Using Backbone Foundation Model for Evaluating Fairness in Chest Radiography Without Demographic Data
Dilermando Queiroz, André Anjos, Lilian Berton
Ensuring consistent performance across diverse populations and incorporating fairness into machine learning models are crucial for advancing medical image diagnostics and promoting…