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Consistent clinical observations of characteristic findings of COVID-19 pneumonia on chest X-rays have attracted the research community to strive to provide a fast and reliable method for screening suspected patients. Several machine learning algorithms have been proposed to find the abnormalities in the lungs using chest X-rays specific to COVID-19 pneumonia and distinguish them from other etiologies of pneumonia. However, despite the enormous magnitude of the pandemic, there are very few instances of public databases of COVID-19 pneumonia, and to the best of our knowledge, there is no database with annotation of abnormalities on the chest X-rays of COVID-19 affected patients. Annotated databases of X-rays can be of significant value in the design and development of algorithms for disease prediction. Further, explainability analysis for the performance of existing or new deep learning algorithms will be enhanced significantly with access to ground-truth abnormality annotations. The proposed COVID Abnormality Annotation for X-Rays (CAAXR) database is built upon the BIMCV-COVID19+ database which is a large-scale dataset containing COVID-19+ chest X-rays. The primary contribution of this study is the annotation of the abnormalities in over 1700 frontal chest X-rays. Further, we define protocols for semantic segmentation as well as classification for robust evaluation of algorithms. We provide benchmark results on the defined protocols using popular deep learning models such as DenseNet, ResNet, MobileNet, and VGG for classification, and UNet, SegNet, and Mask-RCNN for semantic segmentation. The classwise accuracy, sensitivity, and AUC-ROC scores are reported for the classification models, and the IoU and DICE scores are reported for the segmentation models.

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Due to the nonsparse representation, the use of compressed sensing (CS) for physiological signals, such as a multichannel electroencephalogram (EEG), has been a challenge. We present a generalized Bayesian CS framework that is capable of handling representations that arise in the spatiotemporal setting. The proposed model utilizes the standard linear Gaussian observation model associated with the hierarchical modeling of data using the matrix-variate Gaussian scale mixture (GSM). It deploys various random and deterministic parameters to incorporate the knowledge of spatial and temporal correlation present in data. By varying distributions over random parameters, a family of generalized hyperbolic matrix variate distributions is derived. For estimation, we rely on variational Bayes (VB) for random parameters and expectation-maximization (EM) for deterministic parameters. Furthermore, the model is compared with recent developments in matrix-variate distribution-based modeling of data, and we briefly discuss its extension to finite mixtures of skewed distributions. Finally, the framework is applied to the steady-state visual evoked potential (SSVEP)-based EEG benchmark data set, and a comparative study is conducted to show its effectiveness for the frequency detection task. One of the crucial features of the proposed model is that it simultaneously processes multichannel signals with low computational cost and time, making it suitable for real-time systems, especially in a resource-constrained environment.

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With increasing number of COVID-19 cases globally, all the countries are ramping up the testing numbers. While the RT-PCR kits are available in sufficient quantity in several countries, others are facing challenges with limited availability of testing kits and processing centers in remote areas. This has motivated researchers to find alternate methods of testing which are reliable, easily accessible and faster. Chest X-Ray is one of the modalities that is gaining acceptance as a screening modality. Towards this direction, the paper has two primary contributions. Firstly, we present the COVID-19 Multi-Task Network (COMiT-Net) which is an automated end-to-end network for COVID-19 screening. The proposed network not only predicts whether the CXR has COVID-19 features present or not, it also performs semantic segmentation of the regions of interest to make the model explainable. Secondly, with the help of medical professionals, we manually annotate the lung regions and semantic segmentation of COVID19 symptoms in CXRs taken from the ChestXray-14, CheXpert, and a consolidated COVID-19 dataset. These annotations will be released to the research community. Experiments performed with more than 2500 frontal CXR images show that at 90% specificity, the proposed COMiT-Net yields 96.80% sensitivity.

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BACKGROUND: Functional integration or connectivity in brain is directional, non-linear as well as variable in time-lagged dependence. Deep neural networks (DNN) have become an indispensable tool everywhere, by learning higher levels of abstract and complex patterns from raw data. However, in neuroscientific community they generally work as black-boxes, leading to the explanation of results difficult and less intuitive. We aim to propose a brain-connectivity measure based on an explainable NN (xNN) approach. NEW METHOD: We build a NN-based predictor for regression problem. Since we aim to determine the contribution/relevance of past data-point from one region i in the prediction of current data-point from another region j, i.e. the higher-order connectivity between two brain-regions, we employ layer-wise relevance propagation (Bach et al., 2015) (LRP, a method for explaining DNN predictions), which has not been done before to the best of our knowledge. Specifically, we propose a novel score depending on weights as a quantitative measure of connectivity, called as relative relevance score (xNN-RRS). The RRS is an intuitive and transparent score. We provide an interpretation of the trained NN weights with-respect-to the brain-connectivity. RESULTS: Face validity of our approach is demonstrated with experiments on simulated data, over existing methods. We also demonstrate construct validity of xNN-RRS in a resting-state fMRI experiment. COMPARISON: Our approach shows superior performance, in terms of accuracy and computational complexity, over existing state-of-the-art methods for brain-connectivity estimation. CONCLUSION: The proposed method is promising to serve as a first post-hoc explainable NN-approach for brain-connectivity analysis in clinical applications.

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OBJECTIVE: Segmentation of anatomical structures in ultrasound images requires vast radiological knowledge and experience. Moreover, the manual segmentation often results in subjective variations, therefore, an automatic segmentation is desirable. We aim to develop a fully convolutional neural network (FCNN) with attentional deep supervision for the automatic and accurate segmentation of the ultrasound images. METHOD: FCNN/CNNs are used to infer high-level context using low-level image features. In this paper, a sub-problem specific deep supervision of the FCNN is performed. The attention of fine resolution layers is steered to learn object boundary definitions using auxiliary losses, whereas coarse resolution layers are trained to discriminate object regions from the background. Furthermore, a customized scheme for downweighting the auxiliary losses and a trainable fusion layer are introduced. This produces an accurate segmentation and helps in dealing with the broken boundaries, usually found in the ultrasound images. RESULTS: The proposed network is first tested for blood vessel segmentation in liver images. It results in F1 score, mean intersection over union, and dice index of 0.83, 0.83, and 0.79, respectively. The best values observed among the existing approaches are produced by U-net as 0.74, 0.81, and 0.75, respectively. The proposed network also results in dice index value of 0.91 in the lumen segmentation experiments on MICCAI 2011 IVUS challenge dataset, which is near to the provided reference value of 0.93. Furthermore, the improvements similar to vessel segmentation experiments are also observed in the experiment performed to segment lesions. CONCLUSION: Deep supervision of the network based on the input-output characteristics of the layers results in improvement in overall segmentation accuracy. SIGNIFICANCE: Sub-problem specific deep supervision for ultrasound image segmentation is the main contribution of this paper. Currently the network is trained and tested for fixed size inputs. It requires image resizing and limits the performance in small size images.

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Drought is one of the severe abiotic stress that affects the productivity of rice, an important staple crop that is consumed all over the world. The traits responsible for enhancing or adapting drought resistance in rice plants can be selected and studied to improve their growth under stress conditions. Experiments have been conducted on indica rice varieties comprising Sahabhagidhan as drought tolerant variety and IR64, MTU1010 categorized as drought sensitive varieties. Various root related biochemical and morphological traits such as root length, relative water content (RWC), xylem number, xylem area, proline content, and malondialdehyde content have been investigated for a comparative study of the plant response to drought stress in different rice varieties. The results of differential root transcriptome analysis have revealed that there is a notable difference in gene expression of OsPIP2;5 and OsNIP2;1 in various indica varieties of rice at different time periods of stress. The present work aims at assessing the correlation between genotypic and phenotypic traits that can contribute towards the emerging field of rice phenomics.

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OBJECTIVE: Effective connectivity (EC) is the methodology for determining functional-integration among the functionally active segregated regions of the brain. By definition EC is "the causal influence exerted by one neuronal group on another" which is constrained by anatomical connectivity (AC) (axonal connections). AC is necessary for EC but does not fully determine it, because synaptic communication occurs dynamically in a context-dependent fashion. Although there is a vast emerging evidence of structure-function relationship using multimodal imaging studies, till date only a few studies have done joint modeling of the two modalities: functional MRI (fMRI) and diffusion tensor imaging (DTI). We aim to propose a unified probabilistic framework that combines information from both sources to learn EC using dynamic Bayesian networks (DBNs). METHOD: DBNs are probabilistic graphical temporal models that learn EC in an exploratory fashion. Specifically, we propose a novel anatomically informed (AI) score that evaluates fitness of a given connectivity structure to both DTI and fMRI data simultaneously. The AI score is employed in structure learning of DBN given the data. RESULTS: Experiments with synthetic-data demonstrate the face validity of structure learning with our AI score over anatomically uninformed counterpart. Moreover, real-data results are cross-validated by performing classification-experiments. CONCLUSION: EC inferred on real fMRI-DTI datasets is found to be consistent with previous literature and show promising results in light of the AC present as compared to other classically used techniques such as Granger-causality. SIGNIFICANCE: Multimodal analyses provide a more reliable basis for differentiating brain under abnormal/diseased conditions than the single modality analysis.

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Anisotropic diffusion filters are one of the best choices for speckle reduction in the ultrasound images. These filters control the diffusion flux flow using local image statistics and provide the desired speckle suppression. However, inefficient use of edge characteristics results in either oversmooth image or an image containing misinterpreted spurious edges. As a result, the diagnostic quality of the images becomes a concern. To alleviate such problems, a novel anisotropic diffusion-based speckle reducing filter is proposed in this paper. A probability density function of the edges along with pixel relativity information is used to control the diffusion flux flow. The probability density function helps in removing the spurious edges and the pixel relativity reduces the oversmoothing effects. Furthermore, the filtering is performed in superpixel domain to reduce the execution time, wherein a minimum of 15% of the total number of image pixels can be used. For performance evaluation, 31 frames of three synthetic images and 40 real ultrasound images are used. In most of the experiments, the proposed filter shows a better performance as compared to the state-of-the-art filters in terms of the speckle region's signal-to-noise ratio and mean square error. It also shows a comparative performance for figure of merit and structural similarity measure index. Furthermore, in the subjective evaluation, performed by the expert radiologists, the proposed filter's outputs are preferred for the improved contrast and sharpness of the object boundaries. Hence, the proposed filtering framework is suitable to reduce the unwanted speckle and improve the quality of the ultrasound images.

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BACKGROUND: Determination of effective connectivity (EC) among brain regions using fMRI is helpful in understanding the underlying neural mechanisms. Dynamic Bayesian Networks (DBNs) are an appropriate class of probabilistic graphical temporal-models that have been used in past to model EC from fMRI, specifically order-one. NEW-METHOD: High-order DBNs (HO-DBNs) have still not been explored for fMRI data. A fundamental problem faced in the structure-learning of HO-DBN is high computational-burden and low accuracy by the existing heuristic search techniques used for EC detection from fMRI. In this paper, we propose using dynamic programming (DP) principle along with integration of properties of scoring-function in a way to reduce search space for structure-learning of HO-DBNs and finally, for identifying EC from fMRI which has not been done yet to the best of our knowledge. The proposed exact search-&-score learning approach HO-DBN-DP is an extension of the technique which was originally devised for learning a BN's structure from static data (Singh and Moore, 2005). RESULTS: The effectiveness in structure-learning is shown on synthetic fMRI dataset. The algorithm reaches globally-optimal solution in appreciably reduced time-complexity than the static counterpart due to integration of properties. The proof of optimality is provided. COMPARISON: The results demonstrate that HO-DBN-DP is comparably more accurate and faster than currently used structure-learning algorithms used for identifying EC from fMRI. The real data EC from HO-DBN-DP shows consistency with previous literature than the classical Granger Causality method. CONCLUSION: Hence, the DP algorithm can be employed for reliable EC estimates from experimental fMRI data.

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BACKGROUND: Effective connectivity (EC) analysis of neuronal groups using fMRI delivers insights about functional-integration. However, fMRI signal has low-temporal resolution due to down-sampling and indirectly measures underlying neuronal activity. NEW METHOD: The aim is to address above issues for more reliable EC estimates. This paper proposes use of autoregressive hidden Markov model with missing data (AR-HMM-md) in dynamically multi-linked (DML) framework for learning EC using multiple fMRI time series. In our recent work (Dang et al., 2016), we have shown how AR-HMM-md for modelling single fMRI time series outperforms the existing methods. AR-HMM-md models unobserved neuronal activity and lost data over time as variables and estimates their values by joint optimization given fMRI observation sequence. RESULTS: The effectiveness in learning EC is shown using simulated experiments. Also the effects of sampling and noise are studied on EC. Moreover, classification-experiments are performed for Attention-Deficit/Hyperactivity Disorder subjects and age-matched controls for performance evaluation of real data. Using Bayesian model selection, we see that the proposed model converged to higher log-likelihood and demonstrated that group-classification can be performed with higher cross-validation accuracy of above 94% using distinctive network EC which characterizes patients vs. CONTROLS: The full data EC obtained from DML-AR-HMM-md is more consistent with previous literature than the classical multivariate Granger causality method. COMPARISON: The proposed architecture leads to reliable estimates of EC than the existing latent models. CONCLUSIONS: This framework overcomes the disadvantage of low-temporal resolution and improves cross-validation accuracy significantly due to presence of missing data variables and autoregressive process.

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Directionality analysis of time-series, recorded from task-activated regions-of-interest (ROIs) during functional Magnetic Resonance Imaging (fMRI), has helped in gaining insights of complex human behavior and human brain functioning. The most widely used standard method of Granger Causality for evaluating directionality employ linear regression modeling of temporal processes. Such a parameter-driven approach rests on various underlying assumptions about the data. The short-comings can arise when misleading conclusions are reached after exploration of data for which the assumptions are getting violated. In this study, we assess assumptions of Multivariate Autoregressive (MAR) framework which is employed for evaluating directionality among fMRI time-series recorded during a Sensory-Motor (SM) task. The fMRI time-series here is an averaged time-series from a user-defined ROI of multiple voxels. The "aim" is to establish a step-by-step procedure using statistical methods in conjunction with graphical methods to seek the validity of MAR models, specifically in the context of directionality analysis of fMRI data which has not been done previously to the best of our knowledge. Here, in our case of SM task (block design paradigm) there is violation of assumptions, indicating the inadequacy of MAR models to find directional interactions among different task-activated regions of brain.

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The prevalence of HPV genotypes in cervical cancer differs in various regions, though types 16 and 18 generally account for the majority. Knowledge of HPV genotypes in cervical cancer covering the diverse Indian population is important in consideration of the potential future impact of HPV prophylactic vaccination and HPV-based screening strategies. To determine HPV genotype distribution in cervical cancers representing different regions a total of 278 cervical cancer cases were enrolled from cancer centers in North, East, Central and South India. Cervical scrape specimens were tested for HPV DNA using the MY09/11 L1 consensus PCR method followed by sequencing for genotyping, as well as for HPV mRNA utilizing the PreTectTM HPV-Proofer assay. In instances of negative or discrepant results between the two tests, biopsy specimens were tested. HPV DNA and/or mRNA were detected in 91.7% of the cases. Genotype 16 was the most common type, detected alone in 59.4% and in association with type 18 in 3.6% of cases. Genotype 18 was detected as a monotype in 13.3% cases. In total, types 16 and 18 alone or in co-infection with each other were detected in 76.3% cases. Genotype 33 was the third most common type. Overall, genotypes 16, 18, 31, 33, and 45 were the five most common types, detected in 87.1% of the total cases. There were no significant regional differences. In conclusion, the currently available HPV prophylactic vaccines targeting types 16 and 18 have the potential to reduce the burden of cervical cancer in India by over 75%.

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This article proposes a handcrafted fuzzy rule-based system for segmentation and identification of different tissue types in magnetic resonance (MR) brain images. The proposed fuzzy system uses a combination of histogram and spatial neighborhood-based features. The intensity variation from one type of tissue to another is gradual at the boundaries due to the inherent nature of the MR signal (MR physics). A fuzzy rule-based approach is expected to better handle these variations and variability in features corresponding to different types of tissues. The proposed segmentation is tested to classify the pixels of the T2-weighted axial MR images of the brain into three primary tissue types: white matter, gray matter and cerebral-spinal fluid. The results are compared with those from manual segmentation by an expert, demonstrating good agreement between them.

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In this paper, we have proposed a novel scheme for the extraction of textual areas of an image using globally matched wavelet filters. A clustering-based technique has been devised for estim ating globally matched wavelet filters using a collection of groundtruth images. We have extended our text extraction scheme for the segmentation of document images into text, background, and picture components (which include graphics and continuous tone images). Multiple, two-class Fisher classifiers have been used for this purpose. We also exploit contextual information by using a Markov random field formulation-based pixel labeling scheme for refinement of the segmentation results. Experimental results have established effectiveness of our approach.

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In this paper, we design a content-based image retrieval system where multiple query examples can be used to indicate the need to retrieve not only images similar to the individual examples, but also those images which actually represent a combination of the content of query images. We propose a scheme for representing content of an image as a combination of features from multiple examples. This scheme is exploited for developing a multiple example-based retrieval engine. We have explored the use of machine learning techniques for generating the most appropriate feature combination scheme for a given class of images. The combination scheme can be used for developing purposive query engines for specialized image databases. Here, we have considered facial image databases. The effectiveness of the image retrieval system is experimentally demonstrated on different databases.

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Prevalent visualization tools exploit gray value distribution in images through modified histogram equalization and matching technique, referred to as the window width/window level-based method, to improve visibility and enhance diagnostic value. The window width/window level tool is extensively used in magnetic resonance (MR) images to highlight tissue boundaries during image interpretation. However, the identification of different regions and distinct boundaries between them based on gray-level distribution and displayed intensity levels is extremely difficult because of the large dynamic range of tissue intensities inherent in MR images. We propose a soft-segmentation visualization scheme to generate pixel partitions from the histogram of MR image data using a connectionist approach and then generate selective visual depictions of pixel partitions using pseudo color based on an appropriate fuzzy membership function. By applying the display scheme in clinical examples in this study, we could demonstrate additional overlapping regions between distinct tissue types in healthy and diseased areas (in the brain) that could help improve the tissue characterization ability of MR images.

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Most model-based three-dimensional (3-D) object recognition systems use information from a single view of an object. However, a single view may not contain sufficient features to recognize it unambiguously. Further, two objects may have all views in common with respect to a given feature set, and may be distinguished only through a sequence of views. A further complication arises when in an image, we do not have a complete view of an object. This paper presents a new online scheme for the recognition and pose estimation of a large isolated 3-D object, which may not entirely fit in a camera's field of view. We consider an uncalibrated projective camera, and consider the case when the internal parameters of the camera may be varied either unintentionally, or on purpose. The scheme uses a probabilistic reasoning framework for recognition and next-view planning. We show results of successful recognition and pose estimation even in cases of a high degree of interpretation ambiguity associated with the initial view.

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