scholarly journals Augmenting Vascular Disease Diagnosis by Vasculature-aware Unsupervised Learning

2020 ◽  
Author(s):  
Yong Wang ◽  
Mengqi Ji ◽  
Shengwei Jiang ◽  
Xukang Wang ◽  
Jiamin Wu ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
Causes Of Death ◽  
Learning Algorithm ◽  
Vascular Diseases ◽  
Disease Diagnosis ◽  
Vascular Pathology ◽  
Digital Subtraction ◽  
Blood Flows ◽  
Multi Scale ◽  
The Cost

AbstractVascular diseases are among the leading causes of death and threaten human health worldwide. Imaging examination of vascular pathology with reduced invasiveness is challenging due to the intrinsic vasculature complexity and the non-uniform scattering from bio-tissues. Here, we report VasNet, a vasculature-aware unsupervised learning algorithm that augments pathovascular recognition from small sets of unlabeled fluorescence and digital subtraction angiography (DSA) images. The VasNet adopts the multi-scale fusion strategy with a domain adversarial neural network (DANN) loss function that induces biased pattern reconstruction, by strengthening the features relevant to the retinal vasculature reference while weakening the irrelevant features. VasNet delivers outputs of “Structure + X”, where X refers to multi-dimensional features such as blood flows, the distinguishment of blood dilation and its suspicious counterparts, and the dependence of new pattern emergence on a disease progression, which may assist the discovery of novel diagnostics. Therefore, explainable imaging output from VasNet and other algorithm extensions hold the promise to revolutionize the practice of medical diagnosis, as it improves performance while reduces the cost on human expertise, equipment exquisite and time consumption.

scholarly journals Classification and Feature Selection Approaches by Machine Learning Techniques: Heart Disease Prediction

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
N. Satish Chandra Reddy ◽  
Song Shue Nee ◽  
Lim Zhi Min ◽  
Chew Xin Ying
Keyword(s):  
Feature Selection ◽  
Heart Disease ◽  
Causes Of Death ◽  
Disease Diagnosis ◽  
Performance Model ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Heart Disease Diagnosis ◽  
The Cost

The heart disease has been one of the major causes of death worldwide. The heart disease diagnosis has been expensive nowadays, thus it is necessary to predict the risk of getting heart disease with selected features. The feature selection methods could be used as valuable techniques to reduce the cost of diagnosis by selecting the important attributes. The objectives of this study are to predict the classification model, and to know which selected features play a key role in the prediction of heart disease by using Cleveland and statlog project heart datasets. The accuracy of random forest algorithm both in classification and feature selection model has been observed to be 90–95% based on three different percentage splits. The 8 and 6 selected features seem to be the minimum feature requirements to build a better performance model. Whereby, further dropping of the 8 or 6 selected features may not lead to better performance for the prediction model.

scholarly journals Nanostructured Chemiresistive Gas Sensors for Medical Applications

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 462 ◽  
Author(s):  
Noushin Nasiri ◽  
Christian Clarke
Keyword(s):  
Gas Sensors ◽  
Disease Diagnosis ◽  
Medical Diagnostics ◽  
Cost Of Treatment ◽  
Promising Candidate ◽  
Human Breath ◽  
Multi Scale ◽  
Chemiresistive Gas Sensors ◽  
The Cost ◽  
Nanostructured Sensors

Treating diseases at their earliest stages significantly increases the chance of survival while decreasing the cost of treatment. Therefore, compared to traditional blood testing methods it is the goal of medical diagnostics to deliver a technique that can rapidly predict and if required non-invasively monitor illnesses such as lung cancer, diabetes, melanoma and breast cancer at their very earliest stages, when the chance of recovery is significantly higher. To date human breath analysis is a promising candidate for fulfilling this need. Here, we highlight the latest key achievements on nanostructured chemiresistive sensors for disease diagnosis by human breath with focus on the multi-scale engineering of both composition and nano-micro scale morphology. We critically assess and compare state-of-the-art devices with the intention to provide direction for the next generation of chemiresistive nanostructured sensors.

Fruit and Leaves Disease Prediction Using Deep Learning Algorithm

2019 ◽  
pp. 8-16
Author(s):  
Sathya V ◽  
Rafidha H ◽  
Sumitha Rani G
Keyword(s):  
Plant Disease ◽  
Learning Algorithm ◽  
Low Cost ◽  
Disease Diagnosis ◽  
Plant Diseases ◽  
Production Plant ◽  
Deep Learning Algorithm ◽  
Long Time ◽  
The Cost ◽  
And Control

The purpose of Agriculture is not only to feed ever growing population but it’s an important source of energy and a solution to solve the problem of global warming. Plant diseases are extremely significant, as that can adversely affect both quality and quantity of crops in agriculture production. Plant disease diagnosis is very essential in earlier stage in order to cure and control them. Generally the naked eye method is used to identify the diseases. In this method experts are involved who have the ability to detect the changes in leaf color. This method involves lots of efforts, takes long time and also not practical for the large fields. Many times different experts identify the same disease as the different disease. This method is expensive as it requires continuous monitoring of experts. Tree leaves and fruit diseases can increase the cost of agricultural production and may extend to total economic disaster of a producer if not cured appropriately at early stages. The producers need to monitor their crops and detect the first symptoms in order to prevent the spread of a plant disease, with low cost and save the major part of the production. Hiring professional agriculturists may not be affordable especially in remote isolated geographic regions. Machine learning algorithm in image can offer an alternative solution in plant monitoring and such an approach may anyway be controlled by a professional to offer his services with lower cost. It includes image segmentation and image classification approach to predict various types of diseases using Otsu thresholding method and convolutional neural network method.

A Scalable Redefined Stochastic Blockmodel

2021 ◽  
Vol 15 (3) ◽  
pp. 1-28
Author(s):  
Xueyan Liu ◽  
Bo Yang ◽  
Hechang Chen ◽  
Katarzyna Musial ◽  
Hongxu Chen ◽  
...  
Keyword(s):  
Large Scale ◽  
Network Science ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Real World Data ◽  
Computational Overhead ◽  
Stochastic Blockmodel ◽  
Np Hard Problem ◽  
Large Scale Networks ◽  
The Cost

Stochastic blockmodel (SBM) is a widely used statistical network representation model, with good interpretability, expressiveness, generalization, and flexibility, which has become prevalent and important in the field of network science over the last years. However, learning an optimal SBM for a given network is an NP-hard problem. This results in significant limitations when it comes to applications of SBMs in large-scale networks, because of the significant computational overhead of existing SBM models, as well as their learning methods. Reducing the cost of SBM learning and making it scalable for handling large-scale networks, while maintaining the good theoretical properties of SBM, remains an unresolved problem. In this work, we address this challenging task from a novel perspective of model redefinition. We propose a novel redefined SBM with Poisson distribution and its block-wise learning algorithm that can efficiently analyse large-scale networks. Extensive validation conducted on both artificial and real-world data shows that our proposed method significantly outperforms the state-of-the-art methods in terms of a reasonable trade-off between accuracy and scalability. 1

Sparse Incremental Delta-Bar-Delta for System Identification

2014 ◽  
Vol 665 ◽  
pp. 643-646
Author(s):  
Ying Liu ◽  
Yan Ye ◽  
Chun Guang Li
Keyword(s):  
System Identification ◽  
Cost Function ◽  
Learning Algorithm ◽  
Learning System ◽  
The Other ◽  
Sparse System ◽  
Speed Up ◽  
Sparse System Identification ◽  
The Cost ◽  
Zero Attractor

Metalearning algorithm learns the base learning algorithm, targeted for improving the performance of the learning system. The incremental delta-bar-delta (IDBD) algorithm is such a metalearning algorithm. On the other hand, sparse algorithms are gaining popularity due to their good performance and wide applications. In this paper, we propose a sparse IDBD algorithm by taking the sparsity of the systems into account. Thenorm penalty is contained in the cost function of the standard IDBD, which is equivalent to adding a zero attractor in the iterations, thus can speed up convergence if the system of interest is indeed sparse. Simulations demonstrate that the proposed algorithm is superior to the competing algorithms in sparse system identification.

The smallest extraction problem

2021 ◽  
Vol 14 (11) ◽  
pp. 2445-2458
Author(s):  
Valerio Cetorelli ◽  
Paolo Atzeni ◽  
Valter Crescenzi ◽  
Franco Milicchio
Keyword(s):  
Unsupervised Learning ◽  
Optimization Problem ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Data Extraction ◽  
Source Code ◽  
Web Data ◽  
Web Data Extraction ◽  
New Family ◽  
Context Free

We introduce landmark grammars , a new family of context-free grammars aimed at describing the HTML source code of pages published by large and templated websites and therefore at effectively tackling Web data extraction problems. Indeed, they address the inherent ambiguity of HTML, one of the main challenges of Web data extraction, which, despite over twenty years of research, has been largely neglected by the approaches presented in literature. We then formalize the Smallest Extraction Problem (SEP), an optimization problem for finding the grammar of a family that best describes a set of pages and contextually extract their data. Finally, we present an unsupervised learning algorithm to induce a landmark grammar from a set of pages sharing a common HTML template, and we present an automatic Web data extraction system. The experiments on consolidated benchmarks show that the approach can substantially contribute to improve the state-of-the-art.

Abstract 17112: Transgenic Endothelial-Specific HIV-Nef Expression Induces Pulmonary Hypertension and Systemic Vascular Dysfunction, Which Can Be Reverted by Treatment With Statin

Circulation ◽  
2018 ◽  
Vol 138 (Suppl_1) ◽  
Author(s):  
Natalia Bogatcheva ◽  
Sarvesh Chelvanambi ◽  
Xingjuan Chen ◽  
Alexander Obukhov ◽  
Matthias Clauss
Keyword(s):  
Pulmonary Hypertension ◽  
Vascular Dysfunction ◽  
Vascular Diseases ◽  
Arterial Disease ◽  
Statin Treatment ◽  
Systemic Circulation ◽  
Vascular Pathology ◽  
Systemic Hypertension ◽  
Dependent Manner ◽  
Specific Expression

Introduction: HIV patients on ART perplexingly remain at higher risk for developing cardiovascular diseases including acute peripheral arterial disease and pulmonary hypertension. A likely culprit for observed vascular changes is HIV protein Nef, detected both intracellularly and extracellularly in the absence of HIV RNA or DNA. Nef is known to induce endothelial dysfunction through the activation of NADPH; statins are known to inhibit NADPH activation. Hypothesis: Nef expression in endothelial cells will trigger cardiopulmonary and vascular pathology; Nef effects will be reversed by statin. Methods: Endothelial-specific expression of HIV-Nef was achieved by mating the VE-Cadherin-Tet off mice with TRE-Nef mice. The resulting Nef+ double transgenics and their Nef- negative littermates were maintained without doxycycline to induce Nef expression. Changes in pulmonary acceleration and ejection times were analyzed by ultrasound (INVEVO2100). Additionally, we assessed the ability of bradykinin-preconstricted aortic rings to dilate in response to acetylcholine in NO-dependent manner. Results: Between week 10 and week 13 of age, Nef expressing mice displayed gradual reduction of PAT/PET ratio (down to the 75% of the original PAT/PET ratio at week 10), indicative of developing pulmonary hypertension (N=6). PAT/PET ratio in Nef-negative mice did not change significantly between week 10 and 13 of age. Importantly, statin treatment initiated at week 10 completely suppressed PAT/PET changes developing in Nef-expressing mice. Arterial rings from Nef expressing mice (n=4) showed significantly impaired dilatation in response to acetylcholine (10% relaxation in Nef+ mice vs 40% relaxation in Nef-negative littermates, p=0.03), indicative of changes in systemic circulation. This difference was significantly attenuated in Nef+ mice receiving statin treatment. Conclusions: Our data suggests that mice with endothelial expression of HIV-Nef display pathological changes in pulmonary and systemic circulation. Statin treatment significantly attenuates changes in parameters indicative of pulmonary and systemic hypertension, suggesting that statin will be beneficial for patients with HIV-induced cardiopulmonary and vascular diseases.

Some aspects of hemostasis in the tactics of the ophthalmologist. Literature review

2021 ◽  
pp. 265-270
Author(s):  
O.M. Stanishevskaya ◽  
◽  
M.A. Safronova ◽  
G.V. Bratko ◽  
I.Y. Efremova ◽  
...  
Keyword(s):  
Open Angle Glaucoma ◽  
Vascular Diseases ◽  
Laboratory Diagnosis ◽  
The Body ◽  
Vascular Pathology ◽  
Treatment Result ◽  
Important Place ◽  
Correct Orientation ◽  
Pathological Conditions ◽  
Wide Range

Disorders of hemostasis occupy an important place in the structure of vascular diseases and are one of the most frequent pathological conditions encountered in practical medicine. The hemostasis system is naturally the most vulnerable system of the body. Violations of its balance are found in a wide variety of physiological and pathological conditions of the body. It is not uncommon for the first debut of decompensation to lead to an ophthalmologist. In the practice of an ophthalmologist, there are diseases when it is necessary to pay close attention to thrombophilic conditions. Changes in the hemostatic system, affect a wide range of vascular diseases of the eyeball. Recognition of the type of thrombophilia and its timely laboratory diagnosis in patients with vascular diseases of the retina and optic nerve are important in achieving the best treatment results. Multidisciplinary approach to the treatment of vascular diseases of the eyeball and modern diagnostics, including the study of hemostasis, is necessary and relevant to achieve the best clinical and functional treatment result. Timely and correct orientation of patients in vascular pathology is extremely important due to the fact that concomitant systemic pathology can aggravate the course of the disease, therefore, the choice of treatment tactics for this category of patients should be carried out in conjunction with a therapist, cardiologist, hematologist and endocrinologist. Key words: hemostasis, thrombosis CVS, diabet, primary open-angle glaucoma thrombodynamica, cardiovascular pathology.

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