Medical Image Feature Learning Without Manual Annotation

The patent applies self-service by enabling the model to generate its own supervision signals through self-supervised learning. Specifically, the model performs self-training using automatically generated pseudo-labels from unlabeled medical images, eliminating the need for manual annotation while maintaining effective supervision. The system services itself by creating training supervision from the data itself rather than requiring external human annotation resources.

The patent applies copying by creating pseudo-labels that replicate the supervision signals that would be obtained from manual annotation. The self-supervised learning process generates synthetic label copies from the unlabeled data itself, allowing the model to learn from numerous samples without requiring equivalent manual annotation effort for each sample. This copying mechanism enables scaling to large datasets without linearly increasing annotation costs.

The patent applies mechanics substitution by replacing the mechanical process of manual human annotation with an automated computational process. Instead of humans manually labeling images (mechanical human effort), the system uses automated self-supervised learning algorithms to generate supervision signals. This substitution eliminates the need for manual intervention while maintaining effective supervised learning through algorithmic pseudo-label generation and model self-training.