Single-Cell Similarity Encoding for Histopathology Classification

The patent applies preliminary action by pre-training a deep learning model on large-scale unsupervised data (such as ImageNet) to learn general visual features and representations before fine-tuning on the specific task of cell identification in histopathology images. This pre-training phase occurs before any manual annotations are required for the target domain, allowing the model to start with a solid foundation of visual processing capabilities that reduces the need for extensive manual annotations and training time on the specific medical imaging task.

The patent uses copying by transferring pre-trained model weights and architectural designs from general-purpose computer vision models to the specific domain of histopathology image analysis. Instead of training a deep learning model from scratch on annotated medical images, the approach copies and adapts pre-existing models that have already learned effective feature representations from large amounts of unlabeled natural images, thereby significantly reducing the annotation burden and training requirements for the specialized medical application.

The patent applies self-service by using the model's own pre-trained representations and embeddings to automatically generate pseudo-labels for training data without requiring manual annotations. The system leverages its pre-existing knowledge from pre-training to self-generate training labels through techniques such as consistency regularization and self-training, where the model confidently predicts labels for data it is uncertain about and uses these predictions to refine its own training set, thereby reducing dependence on external human annotation while maintaining reliable predictions.

The patent implements feedback mechanisms where the model continuously refines its predictions by comparing them against consistency constraints and using performance metrics to guide further training. The system incorporates feedback loops that adjust model parameters based on prediction confidence and consistency across multiple passes, gradually improving reliability while operating without manual annotations. This feedback-driven refinement allows the unsupervised approach to achieve reliable predictions by iteratively correcting and improving its own output.