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The advent of Deep Learning (DL) is poised to dramatically change the delivery of healthcare in the near future. Not only has DL profoundly affected the healthcare industry it has also influenced global businesses. Within a span of very few years, advances such as self-driving cars, robots performing jobs that are hazardous to human, and chat bots talking with human operators have proved that DL has already made large impact on our lives. The open source nature of DL and decreasing prices of computer hardware will further propel such changes. In healthcare, the potential is immense due to the need to automate the processes and evolve error free paradigms. The sheer quantum of DL publications in healthcare has surpassed other domains growing at a very fast pace, particular in radiology. It is therefore imperative for the radiologists to learn about DL and how it differs from other approaches of Artificial Intelligence (AI). The next generation of radiology will see a significant role of DL and will likely serve as the base for augmented radiology (AR). Better clinical judgement by AR will help in improving the quality of life and help in life saving decisions, while lowering healthcare costs. A comprehensive review of DL as well as its implications upon the healthcare is presented in this review. We had analysed 150 articles of DL in healthcare domain from PubMed, Google Scholar, and IEEE EXPLORE focused in medical imagery only. We have further examined the ethic, moral and legal issues surrounding the use of DL in medical imaging. [ABSTRACT FROM AUTHOR]

Manual ultrasound (US)-based methods are adapted for lumen diameter (LD) measurement to estimate the risk of stroke but they are tedious, error prone, and subjective causing variability. We propose an automated deep learning (DL)-based system for lumen detection. The system consists of a combination of two DL systems: encoder and decoder for lumen segmentation. The encoder employs a 13-layer convolution neural network model (CNN) for rich feature extraction. The decoder employs three up-sample layers of fully convolution network (FCN) for lumen segmentation. Three sets of manual tracings were used during the training paradigm leading to the design of three DL systems. Cross-validation protocol was implemented for all three DL systems. Using the polyline distance metric, the precision of merit for three DL systems over 407 US scans was 99.61%, 97.75%, and 99.89%, respectively. The Jaccard index and Dice similarity of DL lumen segmented region against three ground truth (GT) regions were 0.94, 0.94, and 0.93 and 0.97, 0.97, and 0.97, respectively. The corresponding AUC for three DL systems was 0.95, 0.91, and 0.93. The experimental results demonstrated superior performance of proposed deep learning system over conventional methods in literature. Graphical abstract ᅟ. [ABSTRACT FROM AUTHOR]

Fatty Liver Disease (FLD) is caused by the deposition of fat in liver cells and leads to deadly diseases such as liver cancer. Several FLD detection and characterization systems using machine learning (ML) based on Support Vector Machines (SVM) have been applied. These ML systems utilize large number of ultrasonic grayscale features, pooling strategy for selecting the best features and several combinations of training/testing. As result, they are computationally intensive, slow and do not guarantee high performance due to mismatch between grayscale features and classifier type. This study proposes a reliable and fast Extreme Learning Machine (ELM)-based tissue characterization system (a class of Symtosis) for risk stratification of ultrasound liver images. ELM is used to train single layer feed forward neural network (SLFFNN). The input-to-hidden layer weights are randomly generated reducing computational cost. The only weights to be trained are hidden-to-output layer which is done in a single pass (without any iteration) making ELM faster than conventional ML methods. Adapting four types of K-fold cross-validation ( K = 2, 3, 5 and 10) protocols on three kinds of data sizes: S0-original , S4-four splits , S8-sixty four splits (a total of 12 cases) and 46 types of grayscale features, we stratify the FLD US images using ELM and benchmark against SVM. Using the US liver database of 63 patients (27 normal/36 abnormal), our results demonstrate superior performance of ELM compared to SVM, for all cross-validation protocols ( K2, K3, K5 and K10) and all types of US data sets ( S0, S4, and S8) in terms of sensitivity, specificity, accuracy and area under the curve (AUC). Using the K10 cross-validation protocol on S8 data set, ELM showed an accuracy of 96.75% compared to 89.01% for SVM, and correspondingly, the AUC: 0.97 and 0.91, respectively. Further experiments also showed the mean reliability of 99% for ELM classifier, along with the mean speed improvement of 40% using ELM against SVM. We validated the symtosis system using two class biometric facial public data demonstrating an accuracy of 100%. [ABSTRACT FROM AUTHOR]

A lot of research has been conducted on motion-compensated (MC) de-interlacing, but there are very few publications that discuss the performances of de-interlacing quantatively. The various methods are compared through their performance on known video sequences. Linear system analysis of interlaced video and de-interlacer are proposed in [1]-[3]. It is well established that the performance of the MC methods outperform the fixed or motion-adaptive methods when the motion vectors used are reliable and true to the scene content. Being an open-loop process the performance of the MC de-interlacers degrade drastically when there are motion vector errors. In this paper, a linear system analysis of MC video upconversion systems is presented and the effects of motion vector accuracy on system performance are analyzed. We investigate the various factors that contribute to the motion vector inaccuracy, such as incorrect motion modelling, acceleration between the frames, and insufficient interpolation kernel. [ABSTRACT FROM AUTHOR]

• A new similarity measured is proposed based on statistical mechanics. • The new measure, namely DSSM finds similarity between two documents based on uniqueness of energy. • The DSSM showed higher performance with regards to conventional measures. Machine learning (ML) uses intelligence-based statistical models to resolve characterization problems. However, the accuracy of ML models decreases as the volume of data increases since they are not able to capture the diversity and important regularities accompanying the huge volume. In this regard, the statistical mechanics' (SM) paradigm for ML models must be investigated. SM like Big Data also deals with a large volume of instances. A correlation principle is proposed between data represented as words in a text corpus and a large volume of particles within a system. Based on it a new similarity measure, namely degeneracy-based statistical similarity measure (DSSM) is proposed. The DSSM is directly proportional to the ratio of the total energy of two documents with maximally similar words and the total number of distinct words in the document. The DSSM showed higher performance with regards to existing measures such as Euclidean, Cosine, SMTP, and MBSM. [ABSTRACT FROM AUTHOR]

Data complexity has increased manifold in the age of data-driven societies. The data has become huge and inherently complex. The single-label classification algorithms that were discrete in their operation are losing prominence since the nature of data is not monolithic anymore. There are now cases in machine learning where data may belong to more than one class or multiple classes. This nature of data has created the need for new algorithms or methods that are multi-label in nature. Label distribution learning (LDL) is a new way to view multi-labelled algorithms. It tries to quantify the degree to which a label defines an instance. Therefore, for every instance there is a label distribution. In this paper, we introduce a new learning method, namely, angular label distribution learning (ALDL). It is based on the angular distribution function, which is derived from the computation of the length of the arc connecting two points in a circle. Comparative performance evaluation in terms of mean-square error (MSE) of the proposed ALDL has been made with algorithm adaptation of k-NN (AA-kNN), multilayer perceptron, Levenberg–Marquardt neural network and layer-recurrent neural network LDL datasets. MSE is observed to decrease for the proposed ALDL. ALDL is also highly statistically significant for the real world datasets when compared with the standard algorithms for LDL. [ABSTRACT FROM AUTHOR]

A World Health Organization (WHO) Feb 2018 report has recently shown that mortality rate due to brain or central nervous system (CNS) cancer is the highest in the Asian continent. It is of critical importance that cancer be detected earlier so that many of these lives can be saved. Cancer grading is an important aspect for targeted therapy. As cancer diagnosis is highly invasive, time consuming and expensive, there is an immediate requirement to develop a non-invasive, cost-effective and efficient tools for brain cancer characterization and grade estimation. Brain scans using magnetic resonance imaging (MRI), computed tomography (CT), as well as other imaging modalities, are fast and safer methods for tumor detection. In this paper, we tried to summarize the pathophysiology of brain cancer, imaging modalities of brain cancer and automatic computer assisted methods for brain cancer characterization in a machine and deep learning paradigm. Another objective of this paper is to find the current issues in existing engineering methods and also project a future paradigm. Further, we have highlighted the relationship between brain cancer and other brain disorders like stroke, Alzheimer's, Parkinson's, and Wilson's disease, leukoriaosis, and other neurological disorders in the context of machine learning and the deep learning paradigm. [ABSTRACT FROM AUTHOR]

The original version of this article unfortunately contained a mistake. The family name of Rui Tato Marinho was incorrectly spelled as Marinhoe. [ABSTRACT FROM AUTHOR]

Despite representing a very important class of virulence proteins, the role of lipoproteins in the pathogenesis of Mycobacterium tuberculosis remains elusive. In this study, we investigated the role of putative lipoprotein LprE in the subversion of host immune responses using the M. tuberculosis CDC1551 LprE (LprEMtb) mutant (MtbΔLprE). We show that deletion of LprEMtb results in reduction of M. tuberculosis virulence in human and mouse macrophages due to upregulation of vitamin D3-responsive cathelicidin expression through the TLR2-dependent p38-MAPK-CYP27B1-VDR signaling pathway. Conversely, episomal expression of LprEMtb in Mycobacterium smegmatis improved bacterial survival. Infection in siTLR2-treated or tlr2-/- macrophages reduced the survival of LprEMtb expressing M. tuberculosis and M. smegmatis because of a surge in the expression of cathelicidin. Infection with the LprEMtb mutant also led to accumulation of autophagy-related proteins (LC3, Atg-5, and Beclin-1) and augmented recruitment of phagosomal (EEA1 and Rab7) and lysosomal (LAMP1) proteins, thereby resulting in the reduction of the bacterial count in macrophages. The inhibition of phago-lysosome fusion by LprEMtb was found to be due to downregulation of IL-12 and IL-22 cytokines. Altogether, our data indicate that LprEMtb is an important virulence factor that plays a crucial role in mycobacterial pathogenesis in the context of innate immunity. [ABSTRACT FROM AUTHOR]

We propose a fast and robust 2D-affine global motion estimation algorithm based on phase-correlation in the Fourier-Mellin domain and robust least square model fitting of sparse motion vector field and its application for digital image stabilization. Rotation-scale-translation (RST) approximation of affine parameters is obtained at the coarsest level of the image pyramid, thus ensuring convergence for a much larger range of motions. Despite working at the coarsest resolution level, using subpixel-accurate phase correlation provides sufficiently accurate coarse estimates for the subsequent refinement stage of the algorithm. The refinement stage consists of RANSAC based robust least-square model fitting for sparse motion vector field, estimated using block-based subpixel-accurate phase correlation at randomly selected high activity regions in finest level of image pyramid. Resulting algorithm is very robust to outliers such as foreground objects and flat regions. We investigate the robustness of the proposed method for digital image stabilization application. Experimental results show that the proposed algorithm is capable of estimating larger range of motions as compared to another phase correlation method and optical flow algorithm. [ABSTRACT FROM AUTHOR]

• Identification of high risk differential gene expression using statistical tests. • Development of a machine learning strategy for predicting the cancerous genes. • Four statistical tests and ten machine learning classifiers were experimentally preformed, validated and compared. A colon microarray data is a repository of thousands of gene expressions with different strengths for each cancer cell. It is necessary to detect which genes are responsible for cancer growth. This study presents an exhaustive comparative study of different machine learning (ML) systems which serves two major purposes: (a) identification of high risk differential genes using statistical tests and (b) development of a ML strategy for predicting cancer genes. Four statistical tests namely: Wilcoxon sign rank sum (WCSRS), t test, Kruskal–Wallis (KW), and F-test were adapted for cancerous gene identification using their p-values. The extracted gene set was used to classify cancer patients using ten classifiers namely: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), naïve Bayes (NB), Gaussian process classification (GPC), support vector machine (SVM), artificial neural network (ANN), logistic regression (LR), decision tree (DT), Adaboost (AB), and random forest (RF). Performance was then evaluated using cross-validation protocols and standardized metrics viz. accuracy (ACC) and area under the curve (AUC). The colon cancer dataset consists of 2000 genes from 62 patients (40 cancer vs. 22 control). The overall mean ACC of our ML system using all four statistical tests and all ten classifiers was 90.50 %. The ML system showed an ACC of 99.81% using a combination WCSRS test and RF-based classifier. This is an improvement of 8% over previously published values in literature. RF-based model with statistical tests for detection of high risk genes showed the best performance for accurate cancer classification in multi-center clinical trials. [ABSTRACT FROM AUTHOR]

Mycobacterium tuberculosis (Mtb) inhibits host oxidative stress responses facilitating its survival in macrophages; however, the underlying molecular mechanisms are poorly understood. Here, we identified a Mtb acetyltransferase (Rv3034c) as a novel counter actor of macrophage oxidative stress responses by inducing peroxisome formation. An inducible Rv3034c deletion mutant of Mtb failed to induce peroxisome biogenesis, expression of the peroxisomal β-oxidation pathway intermediates (ACOX1, ACAA1, MFP2) in macrophages, resulting in reduced intracellular survival compared to the parental strain. This reduced virulence phenotype was rescued by repletion of Rv3034c. Peroxisome induction depended on the interaction between Rv3034c and the macrophage mannose receptor (MR). Interaction between Rv3034c and MR induced expression of the peroxisomal biogenesis proteins PEX5p, PEX13p, PEX14p, PEX11β, PEX19p, the peroxisomal membrane lipid transporter ABCD3, and catalase. Expression of PEX14p and ABCD3 was also enhanced in lungs from Mtb aerosol-infected mice. This is the first report that peroxisome-mediated control of ROS balance is essential for innate immune responses to Mtb but can be counteracted by the mycobacterial acetyltransferase Rv3034c. Thus, peroxisomes represent interesting targets for host-directed therapeutics to tuberculosis. [ABSTRACT FROM AUTHOR]