GIT Image Embedding Comparison for Duplicate Event Classification

A machine learning classification system serves as an intermediary between the image data and the clinician. The system automatically compares images using embedding distance metrics, identifies duplicate or similar event indicators, and presents filtered results to the clinician. This intermediary processing reduces the number of images requiring manual review from thousands to a manageable subset, maintaining identification accuracy while dramatically reducing review time.

The patent replaces the mechanical/manual process of image review with an automated machine learning system. The classification engine uses embedding-based comparison algorithms to automatically identify and group images containing the same event indicators. This substitution eliminates the need for clinicians to manually examine thousands of images, reducing review time while the modular architecture of the ML system manages complexity through standardized processing pipelines.

The system performs preliminary action by pre-computing and storing embedding representations for each image in the dataset. These embeddings capture the essential visual features in a compressed vector form. When comparing images, the system only needs to compute distances between pre-computed embeddings rather than performing full image analysis. This preliminary embedding step, though computationally intensive, is performed once and enables rapid subsequent comparisons, improving overall efficiency while managing computational energy through caching and reuse of embedding data.