1 citations · 4 across the 8 of their papers we have counts for
8 papers
The Impact of Skin Tone Label Granularity on the Performance and Fairness of AI Based Dermatology Image Classification Models
Partha Shah, Durva Sankhe, Maariyah Rashid +6
Artificial intelligence (AI) models to automatically classify skin lesions from dermatology images have shown promising performance but also susceptibility to bias by skin tone. Th…
Themed Challenges to Solve Data Scarcity in Africa: A Proposition for Increasing Local Data Collection and Integration
Mubaraq Yakubu, Udunna Anazodo, Maruf Adewole +6
In Africa, the scarcity of computational resources and medical datasets remains a major hurdle to the development and deployment of artificial intelligence (AI) tools in clinical s…
Understanding-informed Bias Mitigation for Fair CMR Segmentation
Tiarna Lee, Esther Puyol-Antón, Bram Ruijsink +6
Artificial intelligence (AI) is increasingly being used for medical imaging tasks. However, there can be biases in AI models, particularly when they are trained using imbalanced tr…
An investigation into the causes of race bias in AI-based cine CMR segmentation
Tiarna Lee, Esther Puyol-Anton, Bram Ruijsink +5
Artificial intelligence (AI) methods are being used increasingly for the automated segmentation of cine cardiac magnetic resonance (CMR) imaging. However, these methods have been s…
RAISE -- Radiology AI Safety, an End-to-end lifecycle approach
M. Jorge Cardoso, Julia Moosbauer, Tessa S. Cook +11
The integration of AI into radiology introduces opportunities for improved clinical care provision and efficiency but it demands a meticulous approach to mitigate potential risks a…
An Investigation Into Race Bias in Random Forest Models Based on Breast DCE-MRI Derived Radiomics Features
Mohamed Huti, Tiarna Lee, Elinor Sawyer +1
Recent research has shown that artificial intelligence (AI) models can exhibit bias in performance when trained using data that are imbalanced by protected attribute(s). Most work…
An investigation into the impact of deep learning model choice on sex and race bias in cardiac MR segmentation
Tiarna Lee, Esther Puyol-Antón, Bram Ruijsink +3
In medical imaging, artificial intelligence (AI) is increasingly being used to automate routine tasks. However, these algorithms can exhibit and exacerbate biases which lead to dis…
A systematic study of race and sex bias in CNN-based cardiac MR segmentation
Tiarna Lee, Esther Puyol-Anton, Bram Ruijsink +2
In computer vision there has been significant research interest in assessing potential demographic bias in deep learning models. One of the main causes of such bias is imbalance in…