Derm Foundation is a machine learning (ML) model that produces embeddings based on dermatology images. The embeddings can be used to efficiently build AI models for dermatology image related tasks, requiring less data and less compute than having to fully train a model without the embeddings or the pretrained model.
Trained on large scale datasets, Derm Foundation helps businesses and institutions in healthcare and life sciences do more with their dermatology data with less data, accelerating their ability to build AI models for dermatology image analysis.
For details about how to use the model and how it was trained, see the Derm Foundation model card.
Common Use Cases
The following sections present some common use cases for the model. You're free to pursue any use case, as long as it adheres to the Health AI Developer Foundations terms of use.
Data-efficient classification
Derm Foundation can be used for data-efficient classification tasks, including:
- Classifying clinical conditions like psoriasis, melanoma, or dermatitis
- Scoring the severity or progression of clinical conditions
- Identifying the body part that the skin is from
- Determining the image quality for dermatological assessment
With a small amount of labelled data, you can train a classifier model on top of Derm Foundation embeddings. Furthermore, the embedding from each skin image only needs to be generated once and can be used as an input for a variety of different classifiers, with very little additional compute.
For an example of how to use the model to train classifiers using the public SCIN Dataset, see the Derm Foundation linear classifier notebook in Colab.