We present a progressive meta-algorithm for single image super resolution of very large and seamless images. Deep single image super resolution backbone networks are capable of upsampling local image patches but are susceptible to severe tiling artifacts when attempting to integrate the patches together into a single large image. For large images such as 4K images, the individual low-resolution patches also comprise a narrow receptive field that lacks context regarding the pixel information from the surrounding patches. Our Progressive meta-algorithm is inspired by prior works in progressive GANs and is designed to resolve inter-patch tiling artifact across varying scales while further incorporating context by expanding the receptive field to include the surrounding patches through multiple rounds of progressive upsampling. Our meta-algorithm is compatible with standard super-resolving backbones and enables super-resolution of very large images such as 4K images while overcoming practical GPU memory limitations with commodity graphics cards. We evaluate our Meta-Algorithm with the challenging task of 16x super resolution for 4096×4096 images using the ESRGAN super-resolving backbone and demonstrate significantly improved image quality versus a baseline patch-based approach as evaluated using the Learned Perceptual Image Patch Similarity (LPIPS) metric.

COVID-19 is an air-borne viral infection, which infects the respiratory system in the human body, and it became a global pandemic in early March 2020. The damage caused by the COVID-19 disease in a human lung region can be identified using Computed Tomography (CT) scans. We present a novel approach in classifying COVID-19 infection and normal patients using a Random Forest (RF) model to train on a combination of Deep Learning (DL) features and Radiomic texture features extracted from CT scans of patient’s lungs. We developed and trained DL models using CNN architectures for extracting DL features. The Radiomic texture features are calculated using CT scans and its associated infection masks. In this work, we claim that the RFs classification using the DL features in conjunction with Radiomic texture features enhances prediction performance. The experiment results show that our proposed models achieve a higher True Positive rate with the average Area Under the Receiver Curve (AUC) of 0.9768, 95% Confidence Interval (CI) [0.9757, 0.9780].

COVID-19 is a novel infectious disease responsible for over 1.2 million deaths worldwide as of November 2020. The need for rapid testing is a high priority and alternative testing strategies including x-ray image classification are a promising area of research. However, at present, public datasets for COVID-19 x-ray images have low data volumes, making it challenging to develop accurate image classifiers. Several recent papers have made use of Generative Adversarial Networks (GANs) in order to increase the training data volumes. But realistic synthetic COVID-19 x-rays remain challenging to generate. We present a novel Mean Teacher + Transfer GAN (MTT-GAN) that generates COVID-19 chest x-ray images of high quality. In order to create a more accurate GAN, we employ transfer learning from the Kaggle pneumonia x-ray dataset, a highly relevant data source orders of magnitude larger than public COVID-19 datasets. Furthermore, we employ the Mean Teacher algorithm as a constraint to improve stability of training. Our qualitative analysis shows that the MTT-GAN generates x-ray images that are greatly superior to a baseline GAN and visually comparable to real x-rays. Although board-certified radiologists can distinguish MTT-GAN fakes from real COVID-19 x-rays, quantitative analysis shows that MTT-GAN greatly improves the accuracy of both a binary COVID-19 classifier as well as a multi-class pneumonia classifier as compared to a baseline GAN. Our classification accuracy is favorable as compared to recently reported results in the literature for similar binary and multi-class COVID-19 screening tasks.