Session 12: \u3cem\u3eActive Learning to Minimize the Possible Risk from Future Epidemics\u3c/em\u3e

In medical imaging informatics, for any future epidemics (e.g., Covid-19), deep learning (DL) models are of no use as they require a large dataset as they take months and even years to collect enough data (with annotations). In such a context, active learning (or human/expert-in-the-loop) is the must, where a machine can learn from the first day with minimum possible labeled data. In unsupervised learning, we propose to build pre-trained DL models that iteratively learn independently over time, where human/expert intervenes only when it makes mistakes and for only a limited data. In our work, deep features are used to classify data into two clusters (0/1: Covid-19/non-Covid-19) on two different image datasets: chest x-ray (CXR) and Computed Tomography (CT) scan of sizes 4,714 and 10,000 CTs, respectively. Using pre-trained DL models and unsupervised learning, in our active learning framework, we received the highest AUC of 0.99 and 0.94 on CXR and CT scan datasets, respectively. Not to be confused, our primary objective is to provide a strong assertion on how active learning could potentially be used to predict disease from any upcoming epidemics

Foreign Object Detection and Localization in Chest X-rays using Deep Learning

Pulmonary abnormalities, such as Tuberculosis (TB), Asthma and/or Chronic obstructive are global threats. Nearly 1.6 million died from TB alone according to the WHO (World Health Organization) report 2019. Computer scientists together with medical experts have designed and reported automated screening systems for chest X-ray (CXR) images. However, most of the research works did not consider detecting foreign objects, such as buttons, coins, ring, pins, bone pieces and other medical devices (e.g. pacemaker) all together that can hinder the performance of automatic screening system. The circle-like foreign objects, such as coins are often confused with nodules, which is one of the primary indicators of Tuberculosis. Thus, in an automated screening process foreign objects need to be separated. Unlike the previous works, we will employ deep learning models, such as Faster R-CNN (Faster Region Proposal Convolutional Neural Network), to detect almost all kinds of foreign objects in CXR images. This research is mainly focused on the detection of foreign objects that are of almost all shapes, sizes and texture in CXR using convolutional neural network. Instead of relying on handcrafted features, we now let machine to find distinguished features to achieve an error as low as possible (technically, 10^-4). We also localize their spatial position in CXR, so that the further process of screening can be advanced and at the same time misdiagnosis and confusion can be eliminated

Towards the ionizing radiation induced bond dissociation mechanism in oxygen, water, guanine and DNA fragmentation: a density functional theory simulation

The radiation-induced damages in bio-molecules are ubiquitous processes in radiotherapy and radio-biology, and critical to space projects. In this study, we present a precise quantification of the fragmentation mechanisms of deoxyribonucleic acid (DNA) and the molecules surrounding DNA such as oxygen and water under non-equilibrium conditions using the first-principle calculations based on density functional theory (DFT). Our results reveal the structural stability of DNA bases and backbone that withstand up to a combined threshold of charge and hydrogen abstraction owing to simultaneously direct and indirect ionization processes. We show the hydrogen contents of the molecules significantly control the stability in the presence of radiation. This study provides comprehensive information on the impact of the direct and indirect induced bond dissociations and DNA damage and introduces a systematic methodology for fine-tuning the input parameters necessary for the large-scale Monte Carlo simulations of radio-biological responses and mitigation of detrimental effects of ionizing radiation

2D Respiratory Sound Analysis to Detect Lung Abnormalities

Abstract. In this paper, we analyze deep visual features from 2D data representation(s) of the respiratory sound to detect evidence of lung abnormalities. The primary motivation behind this is that visual cues are more important in decision-making than raw data (lung sound). Early detection and prompt treatments are essential for any future possible respiratory disorders, and respiratory sound is proven to be one of the biomarkers. In contrast to state-of-the-art approaches, we aim at understanding/analyzing visual features using our Convolutional Neural Networks (CNN) tailored Deep Learning Models, where we consider all possible 2D data such as Spectrogram, Mel-frequency Cepstral Coefficients (MFCC), spectral centroid, and spectral roll-off. In our experiments, using the publicly available respiratory sound database named ICBHI 2017 (5.5 hours of recordings containing 6898 respiratory cycles from 126 subjects), we received the highest performance with the area under the curve of 0.79 from Spectrogram as opposed to 0.48 AUC from the raw data from a pre-trained deep learning model: VGG16. Our study proved that 2D data representation could help better understand/analyze lung abnormalities as compared to 1D data. In addition, our results can be compared with previous works. Keywords: Lung Abnormality· Respiratory Sound · 2D Data Representation · Deep Visual Features

First-Principles Prediction of New 2D p-SiPN: A Wide Bandgap Semiconductor

Pentagonal two-dimensional ternary sheets are an emerging class of materials because of their novel characteristic and wide range of applications. In this work, we use first-principles density functional theory (DFT) calculations to identify a new pentagonal SiPN, p-SiPN, which is geometrically, thermodynamically, dynamically, and mechanically stable, and has promising experimental potential. The new p-SiPN shows an indirect bandgap semiconducting behavior that is highly tunable with applied equ-biaxial strain. It is mechanically isotropic, along the x-y in-plane direction, and is a soft material possessing high elasticity and ultimate strain. In addition, its exceptional anisotropic optical response with strong UV light absorbance, and small reflectivity and electron energy loss make it a potential material for optoelectronics and nanomechanics

Clinico-histopathological analysis of orbito-ocular lesions: a hospital-based study

Introductions: Preoperative diagnosis of orbital and ocular lesions is necessary for optimum treatment. The study aims to analyze the histomorphological spectrum of orbito-ocular lesions and to evaluate the need of ancillary techniques for confirmation of diagnosis. Methods: A cross sectional hospital based study of orbito-ocular surgical biopsy samples obtained in the Department of Pathology, at Birat Medical College Teaching Hospital, Nepal during one-year period was analysed for clinical and histopathological findings. Demographic data, site and tissue type, benign or malignant, recommendations for special stains and immunohistochemistry panel study were analysed.    Results: Out of 185 total samples, male to female ratio of 1.1:1, age ranged from ten month to 82 years, 11-20 year age group had 39 (21.1%) orbito-ocular lesions and cornea-conjunctiva was involved in 104 (56.2%). Clinical diagnosis correlated well with histopathological diagnosis, p<0.001. The non-neoplastic, benign and malignant lesions were 36.7%, 33.5% and 29.7% respectively. Squamous cell carcinoma was seen in 28 (50.9%) of malignant lesions followed by sebaceous carcinoma 7 (12.7%). The special stains and immunohistochemistry panel was recommended in 38 (20.5% and 21 (11.3%) cases respectively. Conclusions: Findings suggest the clinical and histopathological diagnosis correlated well in diagnosis of a wide spectrum of orbito-ocular lesions. Keywords: ancillary techniques, clincio-pathological correlation, immunohistochemistry, orbito-ocular lesions, squamous cell carcinom

Large Negative Poisson\u27s Ratio and Anisotropic Mechanics in New Penta-PBN Monolayer

The scarce negative Poisson\u27s ratio (NPR) in a two-dimensional (2D) material is an exceptional auxetic property that offers an opportunity to develop nanoscale futuristic multi-functional devices and has been drawing extensive research interest. Inspired by the buckled pentagonal iso-structures that often expose NPR, we employ state-of-the-art first-principles density functional theory calculations and analyses to predict a new 2D metallic ternary auxetic penta-phosphorus boron nitride (p-PBN) with a high value of NPR. The new p-PBN is stable structurally, mechanically, and dynamically and sustainable at room temperature, with experimental feasibility. The short and strong quasi sp3-hybridized B-N bond and unique bond variation and geometrical reconstruction with an applied strain allow p-PBN to inherit a high value of NPR (-0.236) along the (010) direction, the highest among any other ternary penta iso-structures reported to date. Despite having a small elastic strength, the highly asymmetric Young\u27s modulus and Poisson\u27s ratio along the (100) and (010) directions indicate large anisotropic mechanics, which are crucial for potential applications in nanomechanics and nanoauxetics

Electronic structure and estimation of Curie temperature in Ca\u3csub\u3e2\u3c/sub\u3eBIrO\u3csub\u3e6\u3c/sub\u3e(B = Cr, Fe) double perovskites

We investigate the electronic and magnetic properties of Ca 2 CrIrO 6 and Ca 2 FeIrO 6 by means of density functional theory. These materials belong to a family of recently synthesized Ca 2 CrOsO 6 whose properties show possible applications in a room temperature regime. Upon replacement of Os by Ir in Ca 2 CrOsO 6, we found the system to exhibit a stable ferrimagnetic configuration with a bandgap of ∼0.25 eV and an effective magnetic moment of ∼2.58 μ B per unit cell. Furthermore, when chemical doping is considered by replacing Cr with Fe and Os with Ir, the material retains the insulating state but with a reduced bandgap of 0.13 eV and large increment in the effective magnetic moment of ∼6.68 μ B per unit cell. These observed behaviors are noted to be the consequence of the cooperative effect of spin-orbit coupling; Coulomb correlations from Cr-3d, Fe-3d, and Ir-5d electrons; and the crystal field effect of the materials. These calculations suggest that by chemical tuning, one can manipulate the bandgap and their effective magnetic moment, which may help in material fabrication for device applications. To check further the suitability and applicability of Ca 2 CrIrO 6 and Ca 2 FeIrO 6 at higher temperatures, we estimate the Curie temperature (T C) by calculating the spin-exchange coupling. We found that our findings are in a valid T C trend similar to other perovskites. Our findings are expected to be useful in experimental synthesis and transport measurement for potential applications in modern technological devices