Comparing Deep Neural Networks and Gradient Boosting for Pneumonia Detection Using Chest X-Rays

2022 ◽  
pp. 58-79
Author(s):  
Son Nguyen ◽  
Matthew Quinn ◽  
Alan Olinsky ◽  
John Quinn
Keyword(s):  
Neural Networks ◽  
Prediction Accuracy ◽  
Deep Neural Networks ◽  
Classical Model ◽  
Gradient Boosting ◽  
X Rays ◽  
Computational Power ◽  
Computing Services ◽  
Cloud Computing Services ◽  
Machine Learning Models

In recent years, with the development of computational power and the explosion of data available for analysis, deep neural networks, particularly convolutional neural networks, have emerged as one of the default models for image classification, outperforming most of the classical machine learning models in this task. On the other hand, gradient boosting, a classical model, has been widely used for tabular structure data and leading data competitions, such as those from Kaggle. In this study, the authors compare the performance of deep neural networks with gradient boosting models for detecting pneumonia using chest x-rays. The authors implement several popular architectures of deep neural networks, such as Resnet50, InceptionV3, Xception, and MobileNetV3, and variants of a gradient boosting model. The authors then evaluate these two classes of models in terms of prediction accuracy. The computation in this study is done using cloud computing services offered by Google Colab Pro.

scholarly journals Towards computer-aided severity assessment via deep neural networks for geographic and opacity extent scoring of SARS-CoV-2 chest X-rays

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
A. Wong ◽  
Z. Q. Lin ◽  
L. Wang ◽  
A. G. Chung ◽  
B. Shen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Monte Carlo ◽  
Lung Disease ◽  
Disease Severity ◽  
Deep Neural Networks ◽  
Cross Validation ◽  
X Rays ◽  
Computer Aided ◽  
Monte Carlo Cross Validation ◽  
Validation Experiments

AbstractA critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause for the coronavirus disease 2019 (COVID-19) pandemic, is the assessment of the severity of disease progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important assessment metrics being extent of lung involvement and degree of opacity. In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system. Data consisted of 396 CXRs from SARS-CoV-2 positive patient cases. Geographic extent and opacity extent were scored by two board-certified expert chest radiologists (with 20+ years of experience) and a 2nd-year radiology resident. The deep neural networks used in this study, which we name COVID-Net S, are based on a COVID-Net network architecture. 100 versions of the network were independently learned (50 to perform geographic extent scoring and 50 to perform opacity extent scoring) using random subsets of CXRs from the study, and we evaluated the networks using stratified Monte Carlo cross-validation experiments. The COVID-Net S deep neural networks yielded R$$^2$$ 2 of $$0.664 \pm 0.032$$ 0.664 ± 0.032 and $$0.635 \pm 0.044$$ 0.635 ± 0.044 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively, in stratified Monte Carlo cross-validation experiments. The best performing COVID-Net S networks achieved R$$^2$$ 2 of 0.739 and 0.741 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively. The results are promising and suggest that the use of deep neural networks on CXRs could be an effective tool for computer-aided assessment of SARS-CoV-2 lung disease severity, although additional studies are needed before adoption for routine clinical use.

414 Deep Neural Networks: A Survey Tool for Obstructive Sleep Apnea Prediction

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A164-A164
Author(s):  
Pahnwat Taweesedt ◽  
JungYoon Kim ◽  
Jaehyun Park ◽  
Jangwoon Park ◽  
Munish Sharma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Deep Neural Networks ◽  
Support Vector ◽  
Learning Models ◽  
Obstructive Sleep ◽  
Screening Questionnaires ◽  
Machine Learning Models

Abstract Introduction Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder with an estimation of one billion people. Full-night polysomnography is considered the gold standard for OSA diagnosis. However, it is time-consuming, expensive and is not readily available in many parts of the world. Many screening questionnaires and scores have been proposed for OSA prediction with high sensitivity and low specificity. The present study is intended to develop models with various machine learning techniques to predict the severity of OSA by incorporating features from multiple questionnaires. Methods Subjects who underwent full-night polysomnography in Torr sleep center, Texas and completed 5 OSA screening questionnaires/scores were included. OSA was diagnosed by using Apnea-Hypopnea Index ≥ 5. We trained five different machine learning models including Deep Neural Networks with the scaled principal component analysis (DNN-PCA), Random Forest (RF), Adaptive Boosting classifier (ABC), and K-Nearest Neighbors classifier (KNC) and Support Vector Machine Classifier (SVMC). Training:Testing subject ratio of 65:35 was used. All features including demographic data, body measurement, snoring and sleepiness history were obtained from 5 OSA screening questionnaires/scores (STOP-BANG questionnaires, Berlin questionnaires, NoSAS score, NAMES score and No-Apnea score). Performance parametrics were used to compare between machine learning models. Results Of 180 subjects, 51.5 % of subjects were male with mean (SD) age of 53.6 (15.1). One hundred and nineteen subjects were diagnosed with OSA. Area Under the Receiver Operating Characteristic Curve (AUROC) of DNN-PCA, RF, ABC, KNC, SVMC, STOP-BANG questionnaire, Berlin questionnaire, NoSAS score, NAMES score, and No-Apnea score were 0.85, 0.68, 0.52, 0.74, 0.75, 0.61, 0.63, 0,61, 0.58 and 0,58 respectively. DNN-PCA showed the highest AUROC with sensitivity of 0.79, specificity of 0.67, positive-predictivity of 0.93, F1 score of 0.86, and accuracy of 0.77. Conclusion Our result showed that DNN-PCA outperforms OSA screening questionnaires, scores and other machine learning models. Support (if any):

scholarly journals Critical Assessment of Artificial Intelligence Methods for Prediction of hERG Channel Inhibition in the ‘Big Data’ Era

2020 ◽  
Author(s):  
Vishal Babu Siramshetty ◽  
Dac-Trung Nguyen ◽  
Natalia J. Martinez ◽  
Anton Simeonov ◽  
Noel T. Southall ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Big Data ◽  
Recurrent Neural Networks ◽  
Deep Neural Networks ◽  
Prediction Models ◽  
Chemical Space ◽  
Superior Performance ◽  
Gradient Boosting ◽  
Artificial Intelligence Methods

The rise of novel artificial intelligence methods necessitates a comparison of this wave of new approaches with classical machine learning for a typical drug discovery project. Inhibition of the potassium ion channel, whose alpha subunit is encoded by human Ether-à-go-go-Related Gene (hERG), leads to prolonged QT interval of the cardiac action potential and is a significant safety pharmacology target for the development of new medicines. Several computational approaches have been employed to develop prediction models for assessment of hERG liabilities of small molecules including recent work using deep learning methods. Here we perform a comprehensive comparison of prediction models based on classical (random forests and gradient boosting) and modern (deep neural networks and recurrent neural networks) artificial intelligence methods. The training set (~9000 compounds) was compiled by integrating hERG bioactivity data from ChEMBL database with experimental data generated from an in-house, high-throughput thallium flux assay. We utilized different molecular descriptors including the latent descriptors, which are real-valued continuous vectors derived from chemical autoencoders trained on a large chemical space (> 1.5 million compounds). The models were prospectively validated on ~840 in-house compounds screened in the same thallium flux assay. The deep neural networks performed significantly better than the classical methods with the latent descriptors. The recurrent neural networks that operate on SMILES provided highest model sensitivity. The best models were merged into a consensus model that offered superior performance compared to reference models from academic and commercial domains. Further, we shed light on the potential of artificial intelligence methods to exploit the chemistry big data and generate novel chemical representations useful in predictive modeling and tailoring new chemical space.<br>

Chapter 15. Human-Centered Concept Explanations for Neural Networks

2021 ◽  
Author(s):  
Chih-Kuan Yeh ◽  
Been Kim ◽  
Pradeep Ravikumar
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Case Studies ◽  
Real World ◽  
Deep Neural Networks ◽  
Learning Models ◽  
Real World Applications ◽  
The Right ◽  
Concept Activation ◽  
Machine Learning Models

Understanding complex machine learning models such as deep neural networks with explanations is crucial in various applications. Many explanations stem from the model perspective, and may not necessarily effectively communicate why the model is making its predictions at the right level of abstraction. For example, providing importance weights to individual pixels in an image can only express which parts of that particular image is important to the model, but humans may prefer an explanation which explains the prediction by concept-based thinking. In this work, we review the emerging area of concept based explanations. We start by introducing concept explanations including the class of Concept Activation Vectors (CAV) which characterize concepts using vectors in appropriate spaces of neural activations, and discuss different properties of useful concepts, and approaches to measure the usefulness of concept vectors. We then discuss approaches to automatically extract concepts, and approaches to address some of their caveats. Finally, we discuss some case studies that showcase the utility of such concept-based explanations in synthetic settings and real world applications.

scholarly journals MOLI: multi-omics late integration with deep neural networks for drug response prediction

2019 ◽  
Vol 35 (14) ◽  
pp. i501-i509 ◽  
Author(s):  
Hossein Sharifi-Noghabi ◽  
Olga Zolotareva ◽  
Colin C Collins ◽  
Martin Ester
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Prediction Accuracy ◽  
Drug Response ◽  
Deep Neural Networks ◽  
Response Prediction ◽  
Supplementary Information ◽  
Precision Oncology

Abstract Motivation Historically, gene expression has been shown to be the most informative data for drug response prediction. Recent evidence suggests that integrating additional omics can improve the prediction accuracy which raises the question of how to integrate the additional omics. Regardless of the integration strategy, clinical utility and translatability are crucial. Thus, we reasoned a multi-omics approach combined with clinical datasets would improve drug response prediction and clinical relevance. Results We propose MOLI, a multi-omics late integration method based on deep neural networks. MOLI takes somatic mutation, copy number aberration and gene expression data as input, and integrates them for drug response prediction. MOLI uses type-specific encoding sub-networks to learn features for each omics type, concatenates them into one representation and optimizes this representation via a combined cost function consisting of a triplet loss and a binary cross-entropy loss. The former makes the representations of responder samples more similar to each other and different from the non-responders, and the latter makes this representation predictive of the response values. We validate MOLI on in vitro and in vivo datasets for five chemotherapy agents and two targeted therapeutics. Compared to state-of-the-art single-omics and early integration multi-omics methods, MOLI achieves higher prediction accuracy in external validations. Moreover, a significant improvement in MOLI’s performance is observed for targeted drugs when training on a pan-drug input, i.e. using all the drugs with the same target compared to training only on drug-specific inputs. MOLI’s high predictive power suggests it may have utility in precision oncology. Availability and implementation https://github.com/hosseinshn/MOLI. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Epistatic Net allows the sparse spectral regularization of deep neural networks for inferring fitness functions

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Amirali Aghazadeh ◽  
Hunter Nisonoff ◽  
Orhan Ocal ◽  
David H. Brookes ◽  
Yijie Huang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Coding Theory ◽  
Prediction Accuracy ◽  
Deep Neural Networks ◽  
Epistatic Interactions ◽  
Fitness Functions ◽  
Spectral Regularization ◽  
Competing Models ◽  
Combinatorial Mutagenesis ◽  
Recovery Algorithms

AbstractDespite recent advances in high-throughput combinatorial mutagenesis assays, the number of labeled sequences available to predict molecular functions has remained small for the vastness of the sequence space combined with the ruggedness of many fitness functions. While deep neural networks (DNNs) can capture high-order epistatic interactions among the mutational sites, they tend to overfit to the small number of labeled sequences available for training. Here, we developed Epistatic Net (EN), a method for spectral regularization of DNNs that exploits evidence that epistatic interactions in many fitness functions are sparse. We built a scalable extension of EN, usable for larger sequences, which enables spectral regularization using fast sparse recovery algorithms informed by coding theory. Results on several biological landscapes show that EN consistently improves the prediction accuracy of DNNs and enables them to outperform competing models which assume other priors. EN estimates the higher-order epistatic interactions of DNNs trained on massive sequence spaces-a computational problem that otherwise takes years to solve.

scholarly journals Maximizing the Prediction Accuracy in Tweet Sentiment Extraction using Tensor Flow based Deep Neural Networks

2021 ◽  
Vol 3 (2) ◽  
pp. 61-79
Author(s):  
S Thivaharan ◽  
G Srivatsun
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Prediction Accuracy ◽  
Deep Neural Networks ◽  
Modern Technology ◽  
The Other ◽  
Data Generation ◽  
Class Prediction ◽  
The Social ◽  
Communication Devices

The amount of data generated by modern communication devices is enormous, reaching petabytes. The rate of data generation is also increasing at an unprecedented rate. Though modern technology supports storage in massive amounts, the industry is reluctant in retaining the data, which includes the following characteristics: redundancy in data, unformatted records with outdated information, data that misleads the prediction and data with no impact on the class prediction. Out of all of this data, social media plays a significant role in data generation. As compared to other data generators, the ratio at which the social media generates the data is comparatively higher. Industry and governments are both worried about the circulation of mischievous or malcontents, as they are extremely susceptible and are used by criminals. So it is high time to develop a model to classify the social media contents as fair and unfair. The developed model should have higher accuracy in predicting the class of contents. In this article, tensor flow based deep neural networks are deployed with a fixed Epoch count of 15, in order to attain 25% more accuracy over the other existing models. Activation methods like “Relu” and “Sigmoid”, which are specific for Tensor flow platforms support to attain the improved prediction accuracy.

scholarly journals Estimating Time of Driver Arrival with Gradient Boosting Algorithms and Deep Neural Networks

2020 ◽  
pp. 29-38
Author(s):  
Henrik Sergoyan
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Mean Squared Error ◽  
Service Providers ◽  
Gradient Boosting ◽  
Time Of Arrival ◽  
Squared Error ◽  
Transportation Service ◽  
Boosting Algorithms ◽  
Made In

Customer experience and resource management determine the degree to which transportation service providers can compete in today’s heavily saturated markets. The paper investigates and suggests a new methodology to optimize calculations for Estimated Time of Arrival (from now on ETA, meaning the time it will take for the driver to reach the designated location) based on the data provided by GG collected from rides made in 2018. GG is a transportation service providing company, and it currently uses The Open Source Routing Machine (OSRM) which exhibits significant errors in the prediction phase. This paper shows that implementing algorithms such as XGBoost, CatBoost, and Neural Networks for the said task will improve the accuracy of estimation. Paper discusses the benefits and drawbacks of each model and then considers the performance of the stacking algorithm that combines several models into one. Thus, using those techniques, final results showed that Mean Squared Error (MSE) was decreased by 54% compared to the current GG model.

scholarly journals Safety Analysis of Deep Neural Networks

2021 ◽  
Author(s):  
Dario Guidotti
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Computer Science ◽  
Deep Neural Networks ◽  
Safety Analysis ◽  
Learning Models ◽  
Safety Critical ◽  
Machine Learning Models

Deep Neural Networks (DNNs) are popular machine learning models which have found successful application in many different domains across computer science. Nevertheless, providing formal guarantees on the behaviour of neural networks is hard and therefore their reliability in safety-critical domains is still a concern. Verification and repair emerged as promising solutions to address this issue. In the following, I will present some of my recent efforts in this area.

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