A deep learning model is provided that can, among other things, describe a supervised contrastive learning strategy using deep machine learning that can be used in various modalities. For example, on one medical modality, such as clinical labels, the model learns useful features for fine-tuning on another modality, such as biomarker labels. This model reduces the time and labor required to label tasks. To train conventional deep learning architectures, large quantities of labeled data is necessary. In this novel deep learning model, each individual data item, such as an image, is associated with a modality of labels such as clinical information. The model trains the data with a standard cross-entropy loss, a measure of probability distribution, to identify labels. This setup allows the leveraging of other types of data for the task of interest even without access to large amounts of labeled information within the explicit modality. 

This technology is related to 9099 "Deployable Critical Active Learning (DECAL) System Offers Better Characterization for Improved Diagnostics and Imaging”