Potential diagnosis of COVID-19 from chest X-ray and CT findings using semi-supervised learning.

COVID-19 is an infectious disease, which has adversely affected public health and the economy across the world. On account of the highly infectious nature of the disease, rapid automated diagnosis of COVID-19 is urgently needed. A few recent findings suggest that chest X-rays and CT scans can be used by machine learning for the diagnosis of COVID-19. Herein, we employed semi-supervised learning (SSL) approaches to detect COVID-19 cases accurately by analyzing digital chest X-rays and CT scans. On a relatively small COVID-19 radiography dataset, which contains only 219 COVID-19 positive images, 1341 normal and 1345 viral pneumonia images, our algorithm, COVIDCon, which takes advantage of data augmentation, consistency regularization, and multicontrastive learning, attains 97.07% average class prediction accuracy, with 1000 labeled images, which is 7.65% better than the next best SSL method, virtual adversarial training. COVIDCon performs even better on a larger COVID-19 CT Scan dataset that contains 82,767 images. It achieved an excellent accuracy of 99.13%, at 20,000 labels, which is 6.45% better than the next best pseudo-labeling approach. COVIDCon outperforms other state-of-the-art algorithms at every label that we have investigated. These results demonstrate COVIDCon as the benchmark SSL algorithm for potential diagnosis of COVID-19 from chest X-rays and CT-Scans. Furthermore, COVIDCon performs exceptionally well in identifying COVID-19 positive cases from a completely unseen repository with a confirmed COVID-19 case history. COVIDCon, may provide a fast, accurate, and reliable method for screening COVID-19 patients.

MultiCon: A Semi-Supervised Approach for Predicting Drug Function from Chemical Structure Analysis.

Semi-supervised learning has proved its efficacy in utilizing extensive unlabeled data to alleviate the use of a large amount of supervised data and improve model performance. Despite its tremendous potential, semi-supervised learning has yet to be implemented in the field of drug discovery. Empirical testing of drugs and their classification is costly and time-consuming. In contrast, predicting therapeutic applications of drugs from their structural formulas using semi-supervised learning would reduce costs and time significantly. Herein, we employ a new multicontrastive-based semi-supervised learning algorithm-MultiCon-for classifying drugs into 12 categories, according to therapeutic applications, on the basis of image analyses of their structural formulas. By rational use of data balancing, online augmentations of the drug image data during training, and the combined use of multicontrastive loss with consistency regularization, MultiCon achieves better class prediction accuracies when compared with the state-of-the-art machine learning methods across a variety of existing semi-supervised learning benchmarks. In particular, it performs exceptionally well with a limited number of labeled examples. For instance, with just 5000 labeled drugs in a PubChem (D3) data set, MultiCon achieved a class prediction accuracy of 97.74%.