Building RadiologyNET: Unsupervised annotation of a large-scale multimodal medical database

TL;DR

This paper presents an unsupervised method to automatically annotate a large-scale medical radiology image database by integrating multimodal data sources and clustering techniques, facilitating improved dataset annotation for machine learning applications.

Contribution

The study introduces a novel multimodal unsupervised annotation pipeline that combines image, metadata, and diagnosis data to cluster and label large medical image datasets automatically.

Findings

Fusing multimodal features yields the best clustering performance.

The pipeline successfully clusters over 1.3 million images into 50 meaningful groups.

Cluster homogeneity and mutual information indicate high-quality annotations.

Abstract

Background and objective: The usage of machine learning in medical diagnosis and treatment has witnessed significant growth in recent years through the development of computer-aided diagnosis systems that are often relying on annotated medical radiology images. However, the availability of large annotated image datasets remains a major obstacle since the process of annotation is time-consuming and costly. This paper explores how to automatically annotate a database of medical radiology images with regard to their semantic similarity. Material and methods: An automated, unsupervised approach is used to construct a large annotated dataset of medical radiology images originating from Clinical Hospital Centre Rijeka, Croatia, utilising multimodal sources, including images, DICOM metadata, and narrative diagnoses. Several appropriate feature extractors are tested for each of the data sources, and their utility is evaluated using k-means and k-medoids clustering on a representative data subset. Results: The optimal feature extractors are then integrated into a multimodal representation, which is then clustered to create an automated pipeline for labelling a precursor dataset of 1,337,926 medical images into 50 clusters of visually similar images. The quality of the clusters is assessed by examining their homogeneity and mutual information, taking into account the anatomical region and modality representation. Conclusion: The results suggest that fusing the embeddings of all three data sources together works best for the task of unsupervised clustering of large-scale medical data, resulting in the most concise clusters. Hence, this work is the first step towards building a much larger and more fine-grained annotated dataset of medical radiology images.