The development of reliable and fair diagnostic systems is often constrained by the scarcity
of labeled data. To address this challenge our work explores the feasibility of unsupervised …

Designing deep learning (DL) models that adapt to new data without forgetting previously
acquired knowledge is important in the medical field where data is generated daily, posing a …

To date, skin cancer is the most commonly diagnosed form of cancer in humans and is one
of the leading causes of death in cancer patients. AI technologies can match and exceed …

This review article discusses the roles of federated learning (FL) and transfer learning (TL) in
cancer detection based on image analysis. These two strategies powered by machine …

Distribution shifts hinder the deployment of deep learning in real-world problems.
Distribution shifts appear when train and test data come from different sources, which …

The potential of deep neural networks in skin lesion classification has already been
demonstrated to be on-par if not superior to the dermatologists' diagnosis in experimental …

The potential of deep neural networks in skin lesion classification has already been
demonstrated to be on-par if not superior to the dermatologists diagnosis. However, the …

Prototypical networks (PN) have emerged as one of multiple effective approaches for few-
shot learning (FSL), even in medical image classification. This study focuses on …

The surge in developing deep learning models for diagnosing skin lesions through image
analysis is notable, yet their clinical black faces challenges. Current dermatology AI models …

Deep learning-based diagnostic system has demonstrated potential in classifying skin
cancer conditions when labeled training example are abundant. However, skin lesion …