Skin cancer is a major public health problem that could benefit from computer-aided
diagnosis to reduce the burden of this common disease. Skin lesion segmentation from …

Despite being responsible for state-of-the-art results in several computer vision and natural
language processing tasks, neural networks have faced harsh criticism due to some of their …

Deep learning has become a popular tool for computer-aided diagnosis using medical
images, sometimes matching or exceeding the performance of clinicians. However, these …

Supervised deep learning needs a large amount of labeled data to achieve high
performance. However, in medical imaging analysis, each site may only have a limited …

Fairness is a critical concern in deep learning especially in healthcare where these models
influence diagnoses and treatment decisions. Although fairness has been investigated in the …

Objective Artificial intelligence (AI) models trained using medical images for clinical tasks
often exhibit bias in the form of subgroup performance disparities. However, since not all …

Network pruning aims to compress models while minimizing loss in accuracy. With the
increasing focus on bias in AI systems, the bias inheriting or even magnification nature of …

Deep learning models have achieved great success in automating skin lesion diagnosis.
However, the ethnic disparity in these models' predictions, where lesions on darker skin …

While deep learning based approaches have demonstrated expert-level performance in
dermatological diagnosis tasks, they have also been shown to exhibit biases toward certain …

Pruning has emerged as a powerful technique for compressing deep neural networks,
reducing memory usage and inference time without significantly affecting overall …