Learning effective embedding for automated COVID-19 prediction from chest X-ray images.

The pandemic that the SARS-CoV-2 originated in 2019 is continuing to cause serious havoc on the global population's health, economy, and livelihood. A critical way to suppress and restrain this pandemic is the early detection of COVID-19, which will help to control the virus. Chest X-rays are one of the more straightforward ways to detect the COVID-19 virus compared to the standard methods like CT scans and RT-PCR diagnosis, which are very complex, expensive, and take much time. Our research on various papers shows that the currently researchers are actively working for an efficient Deep Learning model to produce an unbiased detection of COVID-19 through chest X-ray images. In this work, we propose a novel convolution neural network model based on supervised classification that simultaneously computes identification and verification loss. We adopt a transfer learning approach using pretrained models trained on imagenet dataset such as Alex Net and VGG16 as back-bone models and use data augmentation techniques to solve class imbalance and boost the classifier's performance. Finally, our proposed classifier architecture model ensures unbiased and high accuracy results, outperforming existing deep learning models for COVID-19 detection from chest X-ray images producing State of the Art performance. It shows strong and robust performance and proves to be easily deployable and scalable, therefore increasing the efficiency of analyzing chest X-ray images with high accuracy in detection of Coronavirus.

One shot learning approach for cross spectrum periocular verification.

The use of face mask during the COVID-19 pandemic has increased the popularity of the periocular biometrics in surveillance applications. Despite of the rapid advancements in this area, matching images over cross spectrum is still a challenging problem. Reason may be two-fold 1) variations in image illumination 2) small size of available data sets and/or class imbalance problem. This paper proposes Siamese architecture based convolutional neural networks which works on the concept of one-shot classification. In one shot classification, network requires a single training example from each class to train the complete model which may lead to reduce the need of large dataset as well as doesn't matter whether the dataset is imbalance. The proposed architectures comprise of identical subnetworks with shared weights whose performance is assessed on three publicly available databases namely IMP, UTIRIS and PolyU with four different loss functions namely Binary cross entropy loss, Hinge loss, contrastive loss and Triplet loss. In order to mitigate the inherent illumination variations of cross spectrum images CLAHE was used to preprocess images. Extensive experiments show that the proposed Siamese CNN model with triplet loss function outperforms the states of the art periocular verification methods for cross, mono and multi spectral periocular image matching.

Accurate prediction of virus-host protein-protein interactions via a Siamese neural network using deep protein sequence embeddings.

Prediction and understanding of virus-host protein-protein interactions (PPIs) have relevance for the development of novel therapeutic interventions. In addition, virus-like particles open novel opportunities to deliver therapeutics to targeted cell types and tissues. Given our incomplete knowledge of PPIs on the one hand and the cost and time associated with experimental procedures on the other, we here propose a deep learning approach to predict virus-host PPIs. Our method (Siamese Tailored deep sequence Embedding of Proteins [STEP]) is based on recent deep protein sequence embedding techniques, which we integrate into a Siamese neural network. After showing the state-of-the-art performance of STEP on external datasets, we apply it to two use cases, severe acute respiratory syndrome coronavirus 2 and John Cunningham polyomavirus, to predict virus-host PPIs. Altogether our work highlights the potential of deep sequence embedding techniques originating from the field of NLP as well as explainable artificial intelligence methods for the analysis of biological sequences.

Quantifying prognosis severity of COVID-19 patients from deep learning based analysis of CT chest images.

The COVID-19 pandemic has affected all the countries in the world with its droplet spread mode. The colossal amount of cases has strained all the healthcare systems due to the serious nature of infections especially for people with comorbidities. A very high specificity Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) test is the principal technique in use for diagnosing the COVID-19 patients. Also, CT scans have helped medical professionals in patient severity estimation & progression tracking of COVID-19 virus. In study we present our own extensible COVID-19 viral infection tracking prognosis technique. It uses annotated dataset of CT chest scan slice images created with the help of medical professionals. The annotated dataset contains bounding box coordinates of different features for COVID-19 detection like ground glass opacities, crazy paving pattern, consolidations, lesions etc. We qualitatively identify the severity of the patient for later prognosis stages in our study to assist medical staff for patient prioritization. First we detected COVID-19 positive patients with pre-trained Siamese Neural Network (SNN) which obtained 87.6% accuracy, 87.1% F1-Score & 95.1% AUC scores. These metrics were achieved after removal of 40% quantitatively highly similar images from the COVID-CT dataset. This reduced dataset was further medically annotated with COVID-19 features for bounding box detection. After this we assigned severity scores to detected COVID-19 features and calculated the cumulative severity score for COVID-19 patients. For qualitative patient prioritization with prognosis clinical assistance information, we finally converted this score into a multi-classification problem which obtained 47% weighted-average F1-score.