Health-specific embedding tools for dermatology and pathology (blog.research.google)

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 compared to ground truth information in order to evaluate the linear probe's performance. In order to make this type of embedding model available and drive further development of ML tools in medical imaging, we are excited to release two domain-specific tools for research use: Derm Foundation and Path Foundation . This follows on the strong response we’ve already received from researchers using the CXR Foundation embedding tool for chest radiographs and represents a portion of our expanding research offerings across multiple medical-specialized modalities. These embedding tools take an image as input and produce a numerical vector (the embedding) that is specialized to the domains of dermatology and digital pathology images, respectively. By running a dataset of chest X-ray, dermatology, or pathology images through the respective embedding tool, researchers can obtain embeddings for their own images, and use these embeddings to quickly develop new models for their applications. Path