Health datasets play a crucial role in research and medical education, but it can be challenging to create a dataset that represents the real world. For example, dermatology conditions are diverse in their appearance and severity and manifest differently across skin tones. Yet, existing dermatology image datasets often lack representation of everyday conditions (like rashes, allergies and infections) and skew towards lighter skin tones. Furthermore, race and ethnicity information is frequently missing, hindering our ability to assess disparities or create solutions.
To address these limitations, we are releasing the Skin Condition Image Network (SCIN) dataset in collaboration with physicians at Stanford Medicine. We designed SCIN to reflect the broad range of concerns that people search for online, supplementing the types of conditions typically found in clinical datasets. It contains images across various skin tones and body parts, helping to ensure that future AI tools work effectively for all. We’ve made the SCIN dataset freely available as an open-access resource for researchers, educators, and developers, and have taken careful steps to protect contributor privacy.
 |
| Example set of images and metadata from the SCIN dataset. |
Dataset composition
The SCIN dataset currently contains over 10,000 images of skin, nail, or hair conditions, directly contributed by individuals experiencing them. All contributions were made voluntarily with informed consent by individuals in the US, under an institutional-review board approved study. To provide context for retrospective dermatologist labeling, contributors were asked to take images both close-up and from slightly further away. They were given the option to self-report demographic…
Source link
Read Full Article by Google AI at blog.research.google
Source link
