Title: Learning a Deep Convolution Network with Turing Test Adversaries for Microscopy Image Super Resolution,
Abstract: Adversarially trained deep neural networks have significantly improved
performance of single image super resolution, by hallucinating photorealistic
local textures, thereby greatly reducing the perception difference between a
real high resolution image and its super resolved (SR) counterpart. However,
application to medical imaging requires preservation of diagnostically relevant
features while refraining from introducing any diagnostically confusing
artifacts. We propose using a deep convolutional super resolution network
(SRNet) trained for (i) minimising reconstruction loss between the real and SR
images, and (ii) maximally confusing learned relativistic visual Turing test
(rVTT) networks to discriminate between (a) pair of real and SR images (T1) and
(b) pair of patches in real and SR selected from region of interest (T2). The
adversarial loss of T1 and T2 while backpropagated through SRNet helps it learn
to reconstruct pathorealism in the regions of interest such as white blood
cells (WBC) in peripheral blood smears or epithelial cells in histopathology of
cancerous biopsy tissues, which are experimentally demonstrated here.
Experiments performed for measuring signal distortion loss using peak signal to
noise ratio (pSNR) and structural similarity (SSIM) with variation of SR scale
factors, impact of rVTT adversarial losses, and impact on reporting using SR on
a commercially available artificial intelligence (AI) digital pathology system
substantiate our claims.