Artificial intelligence is poised to rapidly reshape many fields, including that of skin cancer
screening and diagnosis, both as a disruptive and assistive technology. Together with the …

Medical imaging analysis has witnessed remarkable advancements even surpassing
human-level performance in recent years, driven by the rapid development of advanced …

In medical imaging leveraging continual learning (CL) is key for models to adapt to new
classes and data distributions without forgetting prior knowledge. Existing CL methods often …

Dermatological diseases pose a major threat to the global health, affecting almost one-third
of the world's population. Various studies have demonstrated that early diagnosis and …

Deep Learning has shown great success in reshaping medical imaging, yet it faces
numerous challenges hindering widespread application. Issues like catastrophic forgetting …

Skin image analysis plays a fundamental role in the field of dermatology, as it enables early
and accurate detection of various skin conditions. However, this process faces significant …

The ability to incrementally learn to categorize objects is a key feature for a personalized
system in real-world applications. The major constraint for such scenario relies on the …