FAST-CAD: A Fairness-Aware Framework for Non-Contact Stroke Diagnosis

CO-VADA is a new approach aimed at reducing bias in speech emotion recognition (SER) systems. Bias often arises from misleading correlations between speaker characteristics and emotional labels, resulting in unfair predictions across different demographic groups. Unlike many existing methods that require changes to model architecture or demographic annotations, CO-VADA operates without such modifications. It identifies biased training samples and uses voice conversion to generate augmented samples that help the model focus on emotion-relevant features, enhancing fairness in SER systems.

The article discusses a significant advancement in computer vision, focusing on large-scale modality-invariant foundation models for brain MRI analysis. These models utilize self-supervised learning to leverage extensive unlabeled MRI data, enhancing performance in neuroimaging tasks such as lesion segmentation for stroke and epilepsy. The study highlights the importance of maintaining modality-specific features despite successful cross-modality alignment, and the model's code and checkpoints are publicly available.