Image model embeddings for digital pathology and drug development via self-supervised learning

Abstract

Whole slide images (WSIs) contain rich pathology information which can be used to diagnose cancer, characterize the tumour microenvironment (TME), assess patient prognosis, and provide insights into the likelihood of whether a patient may respond to a given treatment. However, since WSI availability is generally scarce during early stage clinical trials, the applicability of deep learning models to new and ongoing drug development in early stages is typically limited. WSIs available in public repositories, such as The Cancer Genome Atlas (TCGA), enable an unsupervised pretraining approach to help alleviate data scarcity. Pretrained models can also be utilised for a range of downstream applications such as automated annotation, quality control (QC), and similar image search. In this work we present DIME (Drug-development Image Model Embeddings), a pipeline for training image patch embeddings for WSIs via self-supervised learning. We compare inpainting and contrastive learning approaches for embedding training in the DIME pipeline, and demonstrate state-of-the-art performance at image patch clustering. In addition, we show that the resultant embeddings allow for training effective downstream patch classifiers with relatively few WSIs, and apply this to an AstraZeneca-sponsored phase III clinical trial. We also highlight the importance of effective colour normalisation for implementing histopathology analysis pipelines, regardless of the core learning algorithm. Finally, we show via subjective exploration of embedding spaces that the DIME pipeline clusters interesting histopathological artefacts, suggesting a possible role for the method in QC pipelines. By clustering image patches according to underlying morphopathologic features, DIME supports subsequent qualitative exploration by pathologists and has the potential to inform and expediate biomarker discovery and drug development.