CCS-GAN: COVID-19 CT-scan classification with very few positive training images. (arXiv:2110.01605v1 [eess.IV])

We present a novel algorithm that is able to classify COVID-19 pneumonia from
CT Scan slices using a very small sample of training images exhibiting COVID-19
pneumonia in tandem with a larger number of normal images. This algorithm is
able to achieve high classification accuracy using as few as 10 positive
training slices (from 10 positive cases), which to the best of our knowledge is
one order of magnitude fewer than the next closest published work at the time
of writing. Deep learning with extremely small positive training volumes is a
very difficult problem and has been an important topic during the COVID-19
pandemic, because for quite some time it was difficult to obtain large volumes
of COVID-19 positive images for training. Algorithms that can learn to screen
for diseases using few examples are an important area of research. We present
the Cycle Consistent Segmentation Generative Adversarial Network (CCS-GAN).
CCS-GAN combines style transfer with pulmonary segmentation and relevant
transfer learning from negative images in order to create a larger volume of
synthetic positive images for the purposes of improving diagnostic
classification performance. The performance of a VGG-19 classifier plus CCS-GAN
was trained using a small sample of positive image slices ranging from at most
50 down to as few as 10 COVID-19 positive CT-scan images. CCS-GAN achieves high
accuracy with few positive images and thereby greatly reduces the barrier of
acquiring large training volumes in order to train a diagnostic classifier for
COVID-19.

Source: https://arxiv.org/abs/2110.01605

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