Machine Learning Biomarker Prediction in Digital Pathology

The patent applies preliminary action by performing biomarker-specific staining only on a subset of training images during the machine learning model training phase, rather than staining all images. The trained model then predicts biomarker presence in additional images without requiring actual staining, thus eliminating the time-consuming staining step for the majority of samples while maintaining identification accuracy.

The patent uses copying by creating virtual staining results through machine learning predictions. Instead of physically staining each tissue sample, the trained model generates predicted staining patterns that replicate the appearance and information of actual biomarker-specific stains, allowing multiple copies of staining information to be obtained from a single physical staining process.

The patent applies universality by training a single machine learning model to predict multiple different biomarkers simultaneously. The model learns to identify patterns associated with various biomarkers from training images stained with different biomarker-specific stains, then applies this learned knowledge to predict the presence of any of these biomarkers in new images without requiring actual multiple staining processes, thus making one model serve multiple detection functions.

The patent uses copying by generating virtual staining results for multiple biomarkers through the trained model. Instead of consuming expensive tissue samples and staining materials for each biomarker detection, the model creates multiple copies of staining information predictions from a single input image, eliminating the need for multiple physical staining processes on the same scarce tissue sample.

The patent applies mechanics substitution by replacing the mechanical staining process with a computational machine learning prediction system. The trained model processes images computationally to predict biomarker presence, substituting the physical chemistry-based staining mechanism with an information-processing approach that can handle large volumes of images rapidly while maintaining diagnostic reliability through the model's learned patterns.

The patent uses preliminary action by performing the computationally intensive model training phase in advance using a relatively small set of stained training images. Once trained, the model can rapidly process numerous additional images without requiring the time-consuming staining process, thus enabling high-throughput screening while maintaining the reliability established during the preliminary training phase.