Machine learning (ML) is pervasively deployed.

Specifically, a set of prototypes is optimized to achieve per-task prototype overfit-ting, enabling accurately obtaining the overfitted prototypes for individual tasks. Furthermore, we introduce a task-guided diffusion process within the prototype space, enabling the meta-learning of a generative process that transitions from a vanilla prototype to an overfitted prototype.

Dermnet-S chooses the top-30 classes for meta-training.

Winter wonderlands are only possible thanks to a sparkly light trick. Few languages have as many distinct words for snow as Japanese, which has words like miyuki or beautiful snow. Breakthroughs, discoveries, and DIY tips sent six days a week. When someone says " as white as snow," it's easy to envision what they're talking about. We often think of snow as a dazzling white, the same way we immediately conjure up a color when someone says "blood red" or "ocean blue."

However, there are only a few datasets that include concept-level meta-labels and most of these meta-labels are relevant for natural images that do not require domain expertise. Previous densely annotated datasets in medicine focused on meta-labels that are relevant to a single disease such as osteoarthritis or melanoma. In dermatology, skin disease is described using an established clinical lexicon that allow clinicians to describe physical exam findings to one another. To provide the first medical dataset densely annotated by domain experts to provide annotations useful across multiple disease processes, we developed SkinCon: a skin disease dataset densely annotated by dermatologists. SkinCon includes 3230 images from the Fitzpatrick 17k skin disease dataset densely annotated with 48 clinical concepts, 22 of which have at least 50 images representing the concept.