Auto-diagnosis of COVID-19 using Lung CT Images with Semi-supervised Shallow Learning Network

In the current world pandemic situation, the contagious Novel Coronavirus Disease 2019 (COVID-19) has raised a real threat to human lives owing to infection on lung cells and human respiratory systems. It is a daunting task for the researchers to find suitable infection patterns on lung CT images for automated diagnosis of COVID-19. A novel integrated semi-supervised shallow neural network framework comprising a Parallel Quantum-Inspired Self-supervised Network (PQIS-Net) for automatic segmentation of lung CT images followed by Fully Connected (FC) layers, is proposed in this article. The proposed PQISNet model is aimed at providing fully automated segmentation of lung CT slices without incorporating pre-trained convolutional neural network based models. A parallel trinity of layered structure of quantum bits are interconnected using an N-connected second order neighborhood-based topology in the suggested PQIS-Net architecture for segmentation of lung CT slices with wide variations of local intensities. A random patch-based classification on PQIS-Net segmented slices is incorporated at the classification layers of the suggested semi-supervised shallow neural network framework. Intensive experiments have been conducted using three publicly available data sets, one for purely segmentation task and the other two for classification (COVID-19 diagnosis). The experimental outcome on segmentation of CT slices using self-supervised PQIS-Net and the diagnosis efficiency (Accuracy, Precision and AUC) of the integrated semi-supervised shallow framework is found to be promising. The proposed model is also found to be superior than the best state of the art techniques and pre-trained convolutional neural network-based models, specially in COVID-19 and Mycoplasma Pneumonia (MP) screening

Incidental Gallbladder Carcinoma: An Eastern Indian Experience and Necessity of Routine Histopathological Examination after All Cholecystectomy

Introduction: The carcinoma of gallbladder is the most common malignancy of the biliary tract. The incidence of this carcinoma varies geographically. Incidental gallbladder carcinoma is diagnosed during histopathological examination after cholecystectomy due to other reasons. Histopathological examination of all cholecystectomy specimens are very essential to rule out these incidental gallbladder carcinomas. Aim: To this study to estimate the frequency of incidental gallbladder carcinoma in patients undergoing routine cholecystectomy and also to evaluate the necessity of routine histopathological examination after all cholecystectomy. Materials and Methods: A retrospective observational study was conducted in Department of Pathology, Midnapore Medical College and Hospital, West Bengal, India between January 2014 to December 2019 (six years) covering 650 patients who underwent laparoscopic and open cholecystectomy. Patients' demographic data, pathologic results, macroscopic appearance of the specimen, cancer staging were recorded and frequency of incidental gallbladder carcinoma were calculated. Result: Total 650 cholecystectomy specimens due to benign gallbladder disease were received in pathology department. Histopathological examination revealed 18 cases of incidental gallbladder carcinoma which comprised 2.8% of all the cholecystectomies. Among them 13 were female and five were in male with male: female ratio of 1:2.6 and the age ranges from 35 to 68 years. Among 18 cases 10 cases showed invasion upto lamina propria (stage T1a), five) cases had invasion in muscular layer (stage T1b) and three cases showed perimuscular connective tissue invasion (stage T2a). Conclution: The present study observed that the incidence of incidental gallbladder carcinoma in cholecystectomy specimen was little higher range in East Indian population and so routine histopathological examination of all cholecystectomy specimens are recommende

Highly Sensitive ppb Level Methanol Sensor by Tuning C:O Ratio of rGO-TiO2 Nanotube Hybrid Structure

In this paper, ppb level methanol sensing by a hybrid gas sensor device based on reduced graphene oxide (rGO) and TiO2 nanotubes is reported. Tuning of carbon to oxygen ratio, in rGO layers, was found to be very effective in modulating the sensor response. The C:O ratio was tuned by varying the voltage (11-31V) during the electro-deposition of rGO (on top of the TiO2 nanotube matrix) and was confirmed by Raman spectroscopy and X-ray photoelectron spectroscopy. Due to variations in the C:O ratio in rGO, the barrier height of rGO-TiO2 nanotube junctions and etch hole dimension on rGO layer were varied. Judicious optimization of these two pivotal parameters resulted in the sensor device (for rGO deposition voltage of 16 V), capable of detecting ppb level (up to 62 ppb) methanol efficiently. The sensor showed ~16%, ~59%, and ~94% response magnitude for 62 ppb, 1 ppm, and 200 ppm concentrations, respectively. A comprehensive discussion elucidating the role of rGO-TiO2 NTs junctions (with a tunable C:O ratio) has also been presented correlating the experimental findings

3-D Quantum-Inspired Self-Supervised Tensor Network for Volumetric Segmentation of Medical Images

This article introduces a novel shallow 3-D self-supervised tensor neural network in quantum formalism for volumetric segmentation of medical images with merits of obviating training and supervision. The proposed network is referred to as the 3-D quantum-inspired self-supervised tensor neural network (3-D-QNet). The underlying architecture of 3-D-QNet is composed of a trinity of volumetric layers, viz., input, intermediate, and output layers interconnected using an S -connected third-order neighborhood-based topology for voxelwise processing of 3-D medical image data, suitable for semantic segmentation. Each of the volumetric layers contains quantum neurons designated by qubits or quantum bits. The incorporation of tensor decomposition in quantum formalism leads to faster convergence of network operations to preclude the inherent slow convergence problems faced by the classical supervised and self-supervised networks. The segmented volumes are obtained once the network converges. The suggested 3-D-QNet is tailored and tested on the BRATS 2019 Brain MR image dataset and the Liver Tumor Segmentation Challenge (LiTS17) dataset extensively in our experiments. The 3-D-QNet has achieved promising dice similarity (DS) as compared with the time-intensive supervised convolutional neural network (CNN)-based models, such as 3-D-UNet, voxelwise residual network (VoxResNet), Dense-Res-Inception Net (DRINet), and 3-D-ESPNet, thereby showing a potential advantage of our self-supervised shallow network on facilitating semantic segmentation