# Health-specific embedding tools for dermatology and pathology

DevFeed: [Health-specific embedding tools for dermatology and pathology](<https://devfeed.tech/articles/health-specific-embedding-tools-for-dermatology-and-pathology-28560.md>)

Original publisher: [Read original article](<http://blog.research.google/2024/03/health-specific-embedding-tools-for.html>)

Author: Google AI (noreply@blogger.com)

Published: 2024-03-08T19:33:00Z

Content type: release

Language: en

Sources: [Google Research](<https://devfeed.tech/sources/google-research.md>)

Topics: [Medical imaging](<https://devfeed.tech/topics/medical-imaging.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [computer-vision](<https://devfeed.tech/tags/computer-vision.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [google](<https://devfeed.tech/tags/google.md>), [health](<https://devfeed.tech/tags/health.md>), [image-classification](<https://devfeed.tech/tags/image-classification.md>), [medical-imaging](<https://devfeed.tech/tags/medical-imaging.md>), [product](<https://devfeed.tech/tags/product.md>), [release](<https://devfeed.tech/tags/release.md>)

## AI overview

Google Research announces Derm Foundation and Path Foundation, two domain-specific embedding tools for research in dermatology and digital pathology. The tools convert medical images into specialized numerical vectors that researchers can use to develop models for downstream applications.

## Source excerpt

Posted by Dave Steiner, Clinical Research Scientist, Google Health, and Rory Pilgrim, Product Manager, Google Research There's a worldwide shortage of access to medical imaging expert interpretation across specialties including radiology, dermatology and pathology. Machine learning (ML) technology can help ease this burden by powering tools that enable doctors to interpret these images more accurately and efficiently. However, the development and implementation of such ML tools are often limited by the availability of high-quality data, ML expertise, and computational resources. One way to catalyze the use of ML for medical imaging is via domain-specific models that utilize deep learning (DL) to capture the information in medical images as compressed numerical vectors (called embeddings). These embeddings represent a type of pre-learned understanding of the important features in an image. Identifying patterns in the embeddings reduces the amount of data, expertise, and compute needed to train performant models as compared to working with high-dimensional data, such as images, directly. Indeed, these embeddings can be used to perform a variety of downstream tasks within the specialized domain (see animated graphic below). This framework of leveraging pre-learned understanding to solve related tasks is similar to that of a seasoned guitar player quickly learning a new song by ear. Because the guitar player has already built up a foundation of skill and understanding, they can quickly pick up the patterns and groove of a new song. Path Foundation is used to convert a small dataset of (image, label) pairs into (embedding, label) pairs. These pairs can then be used to train a task-specific classifier using a linear probe, (i.e., a lightweight linear classifier) as represented in this graphic, or other types of models using the embeddings as input. Once the linear probe is trained, it can be used to make predictions on embeddings from new images. These predictions can be