COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization

Chest X-rays are playing an important role in the testing and diagnosis of COVID-19 disease in the recent pandemic. However, due to the limited amount of labelled medical images, automated classification of these images for positive and negative cases remains the biggest challenge in their reliable use in diagnosis and disease progression. We applied and implemented a transfer learning pipeline for classifying COVID-19 chest X-ray images from two publicly available chest X-ray datasets {https://github.com/ieee8023/covid-chestxray-dataset},{https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia}}. The classifier effectively distinguishes inflammation in lungs due to COVID-19 and pneumonia (viral and bacterial) from the ones with no infection (normal). We have used multiple pre-trained convolutional backbones as the feature extractor and achieved an overall detection accuracy of 91.2% , 95.3%, 96.7% for the VGG16, ResNet50 and EfficientNetB0 backbones respectively. Additionally, we trained a generative adversarial framework (a cycleGAN) to generate and augment the minority COVID-19 class in our approach. For visual explanations and interpretation purposes, we visualized the regions of input that are important for predictions and a gradient class activation mapping (Grad-CAM) technique is used in the pipeline to produce a coarse localization map of the highlighted regions in the image. This activation map can be used to monitor affected lung regions during disease progression and severity stages

Towards real-time detection of squamous pre-cancers from oesophageal endoscopic videos

This study investigates the feasibility of applying state of the art deep learning techniques to detect precancerous stages of squamous cell carcinoma (SCC) cancer in real time to address the challenges while diagnosing SCC with subtle appearance changes as well as video processing speed. Two deep learning models are implemented, which are to determine artefact of video frames and to detect, segment and classify those no-artefact frames respectively. For detection of SCC, both mask-RCNN and YOLOv3 architectures are implemented. In addition, in order to ascertain one bounding box being detected for one region of interest instead of multiple duplicated boxes, a faster non-maxima suppression technique (NMS) is applied on top of predictions. As a result, this developed system can process videos at 16-20 frames per second. Three classes are classified, which are ‘suspicious’, ‘high grade’ and ‘cancer’ of SCC. With the resolution of 1920x1080 pixels of videos, the average processing time while apply YOLOv3 is in the range of 0.064-0.101 seconds per frame, i.e. 10-15 frames per second, while running under Windows 10 operating system with 1 GPU (GeForce GTX 1060). The averaged accuracies for classification and detection are 85% and 74% respectively. Since YOLOv3 only provides bounding boxes, to delineate lesioned regions, mask-RCNN is also evaluated. While better detection result is achieved with 77% accuracy, the classification accuracy is similar to that by YOLOYv3 with 84%. However, the processing speed is more than 10 times slower with an average of 1.2 second per frame due to creation of masks. The accuracy of segmentation by mask-RCNN is 63%. These results are based on the date sets of 350 images. Further improvement is hence in need in the future by collecting, annotating or augmenting more datasets

The influence of ion energy, ion flux, and etch temperature on the electrical and material quality of GaAs etched with an electron cyclotron resonance source

The residual damage incurred to GaAs via etching with a Cl2/Ar plasma generated by an electron cyclotron resonance (ECR) source was investigated as a function of variations in ion energy, ion flux, and etching temperature. The residual damage and electrical properties of GaAs were strongly influenced by changes in these etching parameters. Lattice damage was incurred in all processing situations in the form of small dislocation loops. GaAs etched at high ion energies with 200 W rf power, exhibited a defect density five times higher than GaAs etched at lower ion energies with 20 W rf power. This enhanced residual damage at the higher rf powers was paralleled by a degradation in the unannealed contact resistance. Higher etch rates, which accompany the higher rf power levels, caused the width of the disordered region to contract as the rf power was elevated. Therefore, the residual etch damage is influenced by both the generation and removal of defects. Increasing the microwave power or ion flux resulted in elevating the residual defect density, surface roughness, and unannealed contact resistance. GaAs etched at high temperatures, ∼350 °C, resulted in a lower contact resistance than GaAs etched at 25 °C. The high temperature etching augmented the defect diffusion which in turn lowered the near surface defect density. This decrease in residual damage was deemed responsible for improving the electrical performance at 350 °C. The electrical measurements were found to be more sensitive to the density of defects than the vertical extent of disorder beneath the etched surface. Results of this investigation demonstrate that in order to minimize material damage and improve electrical performance, etching with an ECR source should be performed at low rf and microwave powers with a high substrate temperature. © 1995 American Institute of Physics.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/70988/2/JAPIAU-78-4-2712-1.pd

Crystallization of the FAD-independent acetolactate synthase of Klebsiella pneumoniae

Leucine and valine are formed in a common pathway from pyruvate in which the first intermediate is 2-acetolactate. In some bacteria, this compound also has a catabolic fate as the starting point for the butanediol fermentation. The enzyme (EC 4.1.3.18) that forms 2-acetolactate is known as either acetohydroxyacid synthase (AHAS) or acetolactate synthase (ALS), with the latter name preferred for the catabolic enzyme. A significant difference between AHAS and ALS is that the former requires FAD for catalytic activity, although the reason for this requirement is not well understood. Both enzymes require the cofactor thiamine diphosphate. Here, the crystallization and preliminary X-ray diffraction analysis of the Klebsiella pneumoniae ALS is reported. Data to 2.6 Angstrom resolution have been collected at 100 K using a rotating-anode generator and an R-AXIS IV++ detector. Crystals have unit-cell parameters a = 137.4, b = 143.9, c = 134.4 Angstrom, alpha = 90, beta = 108.4, gamma = 90degrees and belong to space group C2. Preliminary analysis indicates that there are four monomers located in each asymmetric unit

Transfer learning for endoscopy disease detection and segmentation with mask-RCNN benchmark architecture

We proposed and implemented a disease detection and semantic segmentation pipeline using a modified mask-RCNN infrastructure model on the EDD2020 dataset1. On the images provided for the phase-I test dataset, for ’BE’, we achieved an average precision of 51.14%, for ’HGD’ and ’polyp’ it is 50%. However, the detection score for ’suspicious’ and ’cancer’ were low. For phase-I, we achieved a dice coefficient of 0.4562 and an F2 score of 0.4508. We noticed the missed and mis-classification was due to the imbalance between classes. Hence, we applied a selective and balanced augmentation stage in our architecture to provide more accurate detection and segmentation. We observed an increase in detection score to 0.29 on phase-II images after balancing the dataset from our phase-I detection score of 0.24. We achieved an improved semantic segmentation score of 0.62 from our phase-I score of 0.52

Examining the applicability of design methods for large panelized all-wood roof diaphragms under seismic loading

The use of flexible roof diaphragms is very common in the United States, both for residential buildings and large-scale commercial buildings. Due to its simplicity, the traditional diaphragm design method is commonly used in diaphragm design, in particular for the design of diaphragms with relatively small dimensions. The traditional diaphragm design method assumes the axial chord forces developed in framing members under in-plane loading are carried only by the perimeter elements. The traditional diaphragm design method has always been thought to be a conservative design method, especially when applied to large diaphragms. In recent years, the engineering community began to question the applicability of the traditional diaphragm design method. A new design approach known as the collective chord design method was proposed to analyze the chord forces for very large flexible roof diaphragms. This method utilizes strain compatibility of a simple beam to estimate the axial forces in chord members. This paper evaluates the applicability of the traditional and collective chord design methods by modeling the behavior of large panelized roof diaphragms numerically

Crystallization of Arabidopsis thaliana acetohydroxyacid synthase in complex with the sulfonylurea herbicide chlorimuron ethyl

Acetohydroxyacid synthase (AHAS; EC 2.2.1.6) catalyses the formation of 2-acetolactate and 2-aceto-2-hydroxybutyrate as the first step in the biosynthesis of the branched-chain amino acids valine, leucine and isoleucine. The enzyme is inhibited by a wide range of substituted sulfonylureas and imidazolinones and many of these compounds are used as commercial herbicides. Here, the crystallization and preliminary X-ray diffraction analysis of the catalytic subunit of Arabidopsis thaliana AHAS in complex with the sulfonylurea herbicide chlorimuron ethyl are reported. This is the first report of the structure of any plant protein in complex with a commercial herbicide. Crystals diffract to 3.0 Angstrom resolution, have unit-cell parameters a = b = 179.92, c = 185.82 Angstrom and belong to space group P6(4)22. Preliminary analysis indicates that there is one monomer in the asymmetric unit and that these are arranged as pairs of dimers in the crystal. The dimers form a very open hexagonal lattice, with a high solvent content of 81%

The surface plasmon enhancement effect on adsorbed molecules at elevated temperatures

The surface plasmon enhancement effect on adsorbed molecules at elevated substrate temperatures is studied theoretically using surface enhanced Raman scattering (SERS) as an example. The surface structure is idealized to be a monodisperse spherical particle with its nonlocal dielectric response accounted for. The temperature effects are modeled using a temperature-dependent collision frequency in the Drude model. Numerical results show that only a small decrease in the SERS enhancement ratio occurs for temperatures up to the melting point of the substrate, even for scattering close to the surface plasmon resonance frequency of the metal. More definitive results are subjected to more realistic modeling as well as systematic experimental studies. The implication of this result to other surface photochemical processes is discussed

Early detection of oesophageal cancer through colour contrast enhancement for data augmentation

While white light imaging (WLI) of endoscopy has been set as the gold standard for screening and detecting oesophageal squamous cell cancer (SCC), the early signs of SCC are often missed (1 in 4) due to its subtle change of early onset of SCC. This study firstly enhances colour contrast of each of over 600 WLI images and their accompanying narrow band images (NBI) applying CIE colour appearance model CIECAM02. Then these augmented data together with the original images are employed to train a deep learning based system for classification of low grade dysplasia (LGD), SCC and high grade dysplasia (HGD). As a result, the averaged colour difference (ΔE) measured using CIEL*a*b* increased from 11.60 to 14.46 for WLI and from 17.52 to 32.53 for NBI in appearance between suspected regions and their normal neighbours. When training a deep learning system with added enhanced contrasted WLI images, the sensitivity, specific and accuracy for LGD increases by 10.87%, 4.95% and 6.76% respectively. When training with enhanced both WLI and NBI images, these measures for LGD increases by 14.83%, 4.89% and 7.97% respectively, the biggest increase among three classes of SCC, HGD and LGD. In average, the sensitivity, specificity and accuracy for these three classes are 88.26%, 94.44% and 92.63% respectively for classification of SCC, HGD and LGD, being comparable or exceeding existing published work

Si nanostructures fabricated by anodic oxidation with an atomic force microscope and etching with an electron cyclotron resonance source

Nanometer‐scale Si structures have been fabricated by anodic oxidation with an atomic force microscope (AFM) and dry etching using an electron cyclotron resonance (ECR) source. The AFM is used to anodically oxidize a thin surface layer on a H‐passivated (100) Si surface. This oxide is used as a mask for etching in a Cl2 plasma generated by the ECR source. An etch selectivity ≳20 was obtained by adding 20% O2 to the Cl2 plasma. The AFM‐defined mask withstands a 70 nm deep etch, and linewidths∼10 nm have been obtained with a 30 nm etch depth. © 1995 American Institute of Physics.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/70639/2/APPLAB-66-14-1729-1.pd