SYSTEMS AND METHODS FOR TRANSFER-TO-TRANSFER LEARNING-BASED TRAINING OF A MACHINE LEARNING MODEL FOR DETECTING MEDICAL CONDITIONS

摘要

Systems and methods for transfer-to-transfer training using an imbalanced training dataset include reconfiguring an imbalanced training data corpus to a plurality of distinct class-balanced mini-corpora of training data, wherein the reconfiguring includes: (i) partitioning the imbalanced training data corpus into a plurality of mini-corpora of training data samples in which each distinct mini-corpus of the plurality of mini-corpora includes an entirety of the training data samples within the second subset of training data samples; and (ii) allocating an equal number of the training data samples of the first subset into each of the plurality of mini-corpora of training data samples; and transfer-to-transfer learning-based training a subject machine learning algorithm to a trained machine learning model based on implementing the transfer-to-transfer learning-based training using the plurality of distinct class-balanced mini-corpora, wherein in use, the trained machine learning model predicts a presence or a non-presence of COVID-19 based on image data.