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Detection of diseases through medical imaging is preferred due to itsnon-invasive nature. Medical imaging supports multiple modalities of data thatenable a thorough and quick look inside a human body. However, interpretingimaging data is often time-consuming and requires a great deal of humanexpertise. Deep learning models can expedite interpretation and alleviate thework of human experts. However, these models are data-intensive and requiresignificant labeled images for training. During novel disease outbreaks such asCovid-19, we often do not have the required labeled imaging data, especially atthe start of the epidemic. Deep Transfer Learning addresses this problem byusing a pretrained model in the public domain, e.g. any variant of eitherVGGNet, ResNet, Inception, DenseNet, etc., as a feature learner to quicklyadapt the target task from fewer samples. Most pretrained models are deep withcomplex architectures. They are trained with large multi-class datasets such asImageNet, with significant human efforts in architecture design and hyperparameters tuning. We presented 1 a simpler generative source model, pretrainedon a single but related concept, can perform as effectively as existing largerpretrained models. We demonstrate the usefulness of generative transferlearning that requires less compute and training data, for Few Shot Learning(FSL) with a Covid-19 binary classification use case. We compare classic deeptransfer learning with our approach and also report FSL results with threesettings of 84, 20, and 10 training samples. The model implementation ofgenerative FSL for Covid-19 classification is available publicly athttps://github.com/suvarnak/GenerativeFSLCovid.git.

Generative Transfer Learning: Covid-19 Classification with a few Chest X-ray Images