Health-specific embedding tools for dermatology and pathology

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.

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.

The value of domain-specific image representations can also be seen in the figure below, which shows the linear probing performance improvement of Path Foundation (as measured by AUROC) compared to traditional pre-training on natural images (ImageNet-21k). This includes evaluation for tasks such as metastatic breast cancer detection in lymph nodes, prostate cancer grading, and breast cancer grading, among others.

Derm Foundation

Derm Foundation is an embedding tool derived from our research in applying DL to interpret images of dermatology conditions and includes our recent work that adds improvements to generalize better to new datasets. Due to its dermatology-specific pre-training it has a latent understanding of features present in images of skin conditions and can be used to quickly develop models to classify skin conditions. The model underlying the API is a BiT ResNet-101x3 trained in two stages. The first pre-training stage uses contrastive learning, similar to ConVIRT, to train on a large number of image-text pairs from the internet. In the second stage, the image component of this pre-trained model is then fine-tuned for condition classification using clinical datasets, such as those from teledermatology services.

Unlike histopathology images, dermatology images more closely resemble the real-world images used to train many of today's computer vision models. However, for specialized dermatology tasks, creating a high-quality model may still require a large dataset. With Derm Foundation, researchers can use their own smaller dataset to retrieve domain-specific embeddings, and use those to build smaller models (e.g., linear classifiers or other small non-linear models) that enable them to validate their research or product ideas. To evaluate this approach, we trained models on a downstream task using teledermatology data. Model training involved varying dataset sizes (12.5%, 25%, 50%, 100%) to compare embedding-based linear classifiers against fine-tuning.

The modeling variants considered were:

• A linear classifier on frozen embeddings from BiT-M (a standard pre-trained image model)
• Fine-tuned version of BiT-M with an extra dense layer for the downstream task
• A linear classifier on frozen embeddings from the Derm Foundation API
• Fine-tuned version of the model underlying the Derm Foundation API with an extra layer for the downstream task

We found that models built on top of the Derm Foundation embeddings for dermatology-related tasks achieved significantly higher quality than those built solely on embeddings or fine tuned from BiT-M. This advantage was found to be most pronounced for smaller training dataset sizes.

These results demonstrate that the Derm Foundation tooI can serve as a useful starting point to accelerate skin-related modeling tasks. We aim to enable other researchers to build on the underlying features and representations of dermatology that the model has learned.

However, there are limitations with this analysis. We're still exploring how well these embeddings generalize across task types, patient populations, and image settings. Downstream models built using Derm Foundation still require careful evaluation to understand their expected performance in the intended setting.