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Found 6 papers, 1 papers with code
Achieving Fairness Through Channel Pruning for Dermatological Disease Diagnosis
Numerous studies have revealed that deep learning-based medical image classification models may exhibit bias towards specific demographic attributes, such as race, gender, and age.
Toward Fairness via Maximum Mean Discrepancy Regularization on Logits Space
Fairness has become increasingly pivotal in machine learning for high-risk applications such as machine learning in healthcare and facial recognition.
Achieve Fairness without Demographics for Dermatological Disease Diagnosis
To overcome this, we propose a method enabling fair predictions for sensitive attributes during the testing phase without using such information during training.
Toward Fairness Through Fair Multi-Exit Framework for Dermatological Disease Diagnosis
Fairness has become increasingly pivotal in medical image recognition.
Fair Multi-Exit Framework for Facial Attribute Classification
Fairness has become increasingly pivotal in facial recognition.
Representative Image Feature Extraction via Contrastive Learning Pretraining for Chest X-ray Report Generation
Medical report generation is a challenging task since it is time-consuming and requires expertise from experienced radiologists.
