Unbiased Segmentation

Unbiased segmentation aims to develop image and data analysis techniques that avoid perpetuating or amplifying existing biases present in datasets, ensuring fair and accurate results across different demographic groups or data characteristics. Current research focuses on mitigating biases stemming from imbalanced datasets, skewed graph structures, and inherent limitations of model architectures like Graph Convolutional Networks (GCNs) and deep neural networks (DNNs), employing strategies such as data augmentation, learnable graph transformations, and regularization techniques to improve fairness and accuracy. This work is crucial for ensuring the equitable application of AI in sensitive areas like healthcare and social sciences, promoting reliable and trustworthy results across diverse populations.