DeepMCAT: Large-Scale Deep Clustering for Medical Image Categorization
This addresses the challenge of organizing unstructured medical databases like PACS systems, offering a practical solution for hospitals, though it appears incremental as it builds on existing unsupervised methods.
The authors tackled the problem of categorizing large-scale medical image datasets without manual labels by proposing an unsupervised clustering approach, achieving over 0.99 cluster purity on cardiac MR images.
In recent years, the research landscape of machine learning in medical imaging has changed drastically from supervised to semi-, weakly- or unsupervised methods. This is mainly due to the fact that ground-truth labels are time-consuming and expensive to obtain manually. Generating labels from patient metadata might be feasible but it suffers from user-originated errors which introduce biases. In this work, we propose an unsupervised approach for automatically clustering and categorizing large-scale medical image datasets, with a focus on cardiac MR images, and without using any labels. We investigated the end-to-end training using both class-balanced and imbalanced large-scale datasets. Our method was able to create clusters with high purity and achieved over 0.99 cluster purity on these datasets. The results demonstrate the potential of the proposed method for categorizing unstructured large medical databases, such as organizing clinical PACS systems in hospitals.