scholarly journals Imputing Biomarker Status from RWE Datasets—A Comparative Study

2021 ◽  
Vol 11 (12) ◽  
pp. 1356
Author(s):  
Carlos Traynor ◽  
Tarjinder Sahota ◽  
Helen Tomkinson ◽  
Ignacio Gonzalez-Garcia ◽  
Neil Evans ◽  
...  
Keyword(s):  
Missing Data ◽  
Missing Values ◽  
Synthetic Data ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Predictive Mean Matching ◽  
Real World Evidence ◽  
Expectation Maximisation ◽  
Incomplete Datasets ◽  
The Impact

Missing data is a universal problem in analysing Real-World Evidence (RWE) datasets. In RWE datasets, there is a need to understand which features best correlate with clinical outcomes. In this context, the missing status of several biomarkers may appear as gaps in the dataset that hide meaningful values for analysis. Imputation methods are general strategies that replace missing values with plausible values. Using the Flatiron NSCLC dataset, including more than 35,000 subjects, we compare the imputation performance of six such methods on missing data: predictive mean matching, expectation-maximisation, factorial analysis, random forest, generative adversarial networks and multivariate imputations with tabular networks. We also conduct extensive synthetic data experiments with structural causal models. Statistical learning from incomplete datasets should select an appropriate imputation algorithm accounting for the nature of missingness, the impact of missing data, and the distribution shift induced by the imputation algorithm. For our synthetic data experiments, tabular networks had the best overall performance. Methods using neural networks are promising for complex datasets with non-linearities. However, conventional methods such as predictive mean matching work well for the Flatiron NSCLC biomarker dataset.

scholarly journals Geometric Morphometric Data Augmentation Using Generative Computational Learning Algorithms

2020 ◽  
Vol 10 (24) ◽  
pp. 9133
Author(s):  
Lloyd A. Courtenay ◽  
Diego González-Aguilera
Keyword(s):  
Sample Size ◽  
Data Augmentation ◽  
Synthetic Data ◽  
Model Performance ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Geometric Morphometric ◽  
Adversarial Networks ◽  
The Impact

The fossil record is notorious for being incomplete and distorted, frequently conditioning the type of knowledge that can be extracted from it. In many cases, this often leads to issues when performing complex statistical analyses, such as classification tasks, predictive modelling, and variance analyses, such as those used in Geometric Morphometrics. Here different Generative Adversarial Network architectures are experimented with, testing the effects of sample size and domain dimensionality on model performance. For model evaluation, robust statistical methods were used. Each of the algorithms were observed to produce realistic data. Generative Adversarial Networks using different loss functions produced multidimensional synthetic data significantly equivalent to the original training data. Conditional Generative Adversarial Networks were not as successful. The methods proposed are likely to reduce the impact of sample size and bias on a number of statistical learning applications. While Generative Adversarial Networks are not the solution to all sample-size related issues, combined with other pre-processing steps these limitations may be overcome. This presents a valuable means of augmenting geometric morphometric datasets for greater predictive visualization.

scholarly journals Geometric Morphometric Data Augmentation using Generative Computational Learning Algorithms

2020 ◽  
Author(s):  
Lloyd A. Courtenay ◽  
Diego González-Aguilera
Keyword(s):  
Sample Size ◽  
Data Augmentation ◽  
Synthetic Data ◽  
Model Performance ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Geometric Morphometric ◽  
Adversarial Networks ◽  
The Impact

The fossil record is notorious for being incomplete and distorted, frequently conditioning the type of knowledge that can be extracted from it. In many cases, this often leads to issues when performing complex statistical analyses, such as classification tasks, predictive modelling, and variance analyses, such as those used in Geometric Morphometrics. Here different Generative Adversarial Network architectures are experimented with, testing the effects of sample size and domain dimensionality on model performance. For model evaluation, robust statistical methods were used. Each of the algorithms were observed to produce realistic data. Generative Adversarial Networks using different loss functions produced multidimensional synthetic data significantly equivalent to the original training data. Conditional Generative Adversarial Networks were not as successful. The methods proposed are likely to reduce the impact of sample size and bias on a number of statistical learning applications. While Generative Adversarial Networks are not the solution to all sample-size related issues, combined with other pre-processing steps these limitations may be overcome. This presents a valuable means of augmenting geometric morphometric datasets for greater predictive visualization.

Generating Missing Oilfield Data Using A Generative Adversarial Imputation Network GAIN

2021 ◽  
Author(s):  
Justin Andrews ◽  
Sheldon Gorell
Keyword(s):  
Machine Learning ◽  
Missing Data ◽  
Missing Values ◽  
Generative Adversarial Networks ◽  
Imputation Methods ◽  
Adversarial Networks ◽  
Incomplete Observations ◽  
Original Dataset ◽  
Single Field ◽  
Standard Practices

Abstract Missing values and incomplete observations can exist in just about ever type of recorded data. With analytical modeling, and machine learning in particular, the quantity and quality of available data is paramount to acquiring reliable results. Within the oil industry alone, priorities in which data is important can vary from company to company, leading to available knowledge of a single field to vary from place to place. With machine learning requiring very complete sets of data, this issue can require whole portions of data to be discarded in order to create an appropriate dataset. Value imputation has emerged as a valuable solution in cleaning up datasets, and as current technology has advanced new generative machine learning methods have been used to generate images and data that is all but indistinguishable from reality. Using an adaptation of the standard Generative Adversarial Networks (GAN) approach known as a Generative Adversarial Imputation Network (GAIN), this paper evaluates this method and other imputation methods for filling in missing values. Using a gathered fully observed set of data, smaller datasets with randomly masked missing values were generated to validate the effectiveness of the various imputation methods; allowing comparisons to be made against the original dataset. The study found that with various sizes of missing data percentages withing the sets, the "filled in" data could be used with surprising accuracy for further analytics. This paper compares GAIN along with several commonly used imputation methods against more standard practices such as data cropping or filling in with average values for filling in missing data. GAIN, as well as the various imputation methods described are quantified for there ability to fill in data. The study will discuss how the GAIN model can quickly provide the data necessary for analytical studies and prediction of results for future projects.

scholarly journals Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 4953
Author(s):  
Sara Al-Emadi ◽  
Abdulla Al-Ali ◽  
Abdulaziz Al-Ali
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Learning Algorithms ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Networks ◽  
Detection And Identification ◽  
Learning Techniques ◽  
The Impact

Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming concern in regard to the physical infrastructure security, safety and privacy has arisen due to the potential of their use in malicious activities. To address this problem, we propose a novel solution that automates the drone detection and identification processes using a drone’s acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. In this paper, we aim to fill this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio samples using a state-of-the-art deep learning technique known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with different deep learning algorithms, namely, the Convolutional Neural Network, the Recurrent Neural Network and the Convolutional Recurrent Neural Network in drone detection and identification. Moreover, we investigate the impact of our proposed hybrid dataset in drone detection. Our findings prove the advantage of using deep learning techniques for drone detection and identification while confirming our hypothesis on the benefits of using the Generative Adversarial Networks to generate real-like drone audio clips with an aim of enhancing the detection of new and unfamiliar drones.

Intrusion detection of railway clearance from infrared images using generative adversarial networks

2020 ◽  
pp. 1-13
Author(s):  
Yundong Li ◽  
Yi Liu ◽  
Han Dong ◽  
Wei Hu ◽  
Chen Lin
Keyword(s):  
Intrusion Detection ◽  
Synthetic Data ◽  
Generative Adversarial Networks ◽  
Generation Model ◽  
Single Shot ◽  
Data Generation ◽  
Infrared Images ◽  
Adversarial Networks ◽  
Training Samples ◽  
Rgb Images

The intrusion detection of railway clearance is crucial for avoiding railway accidents caused by the invasion of abnormal objects, such as pedestrians, falling rocks, and animals. However, detecting intrusions using deep learning methods from infrared images captured at night remains a challenging task because of the lack of sufficient training samples. To address this issue, a transfer strategy that migrates daytime RGB images to the nighttime style of infrared images is proposed in this study. The proposed method consists of two stages. In the first stage, a data generation model is trained on the basis of generative adversarial networks using RGB images and a small number of infrared images, and then, synthetic samples are generated using a well-trained model. In the second stage, a single shot multibox detector (SSD) model is trained using synthetic data and utilized to detect abnormal objects from infrared images at nighttime. To validate the effectiveness of the proposed method, two groups of experiments, namely, railway and non-railway scenes, are conducted. Experimental results demonstrate the effectiveness of the proposed method, and an improvement of 17.8% is achieved for object detection at nighttime.

scholarly journals Adversarial Data Augmentation on Breast MRI Segmentation

2021 ◽  
Vol 11 (10) ◽  
pp. 4554
Author(s):  
João F. Teixeira ◽  
Mariana Dias ◽  
Eva Batista ◽  
Joana Costa ◽  
Luís F. Teixeira ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Medical Image Analysis ◽  
Synthetic Data ◽  
Breast Mri ◽  
Semantic Segmentation ◽  
Adversarial Networks ◽  
Augmentation Strategy ◽  
Magnetic Resonance Imaging Mri ◽  
Segmentation Task ◽  
The Impact

The scarcity of balanced and annotated datasets has been a recurring problem in medical image analysis. Several researchers have tried to fill this gap employing dataset synthesis with adversarial networks (GANs). Breast magnetic resonance imaging (MRI) provides complex, texture-rich medical images, with the same annotation shortage issues, for which, to the best of our knowledge, no previous work tried synthesizing data. Within this context, our work addresses the problem of synthesizing breast MRI images from corresponding annotations and evaluate the impact of this data augmentation strategy on a semantic segmentation task. We explored variations of image-to-image translation using conditional GANs, namely fitting the generator’s architecture with residual blocks and experimenting with cycle consistency approaches. We studied the impact of these changes on visual verisimilarity and how an U-Net segmentation model is affected by the usage of synthetic data. We achieved sufficiently realistic-looking breast MRI images and maintained a stable segmentation score even when completely replacing the dataset with the synthetic set. Our results were promising, especially when concerning to Pix2PixHD and Residual CycleGAN architectures.

Impact of strong El Niño events on river discharge in South America

2021 ◽  
Author(s):  
Markus Deppner ◽  
Bedartha Goswami
Keyword(s):  
Machine Learning ◽  
Missing Data ◽  
South America ◽  
River Discharge ◽  
Amazon Basin ◽  
Missing Values ◽  
Southern Oscillation ◽  
Enso Events ◽  
Streamflow Data ◽  
The Impact

<p>The impact of the El Niño Southern Oscillation (ENSO) on rivers are well known, but most existing studies involving streamflow data are severely limited by data coverage. Time series of gauging stations fade in and out over time, which makes hydrological large scale and long time analysis or studies of rarely occurring extreme events challenging. Here, we use a machine learning approach to infer missing streamflow data based on temporal correlations of stations with missing values to others with data. By using 346 stations, from the “Global Streamflow Indices and Metadata archive” (GSIM), that initially cover the 40 year timespan in conjunction with Gaussian processes we were able to extend our data by estimating missing data for an additional 646 stations, allowing us to include a total of 992 stations. We then investigate the impact of the 6 strongest El Niño (EN) events on rivers in South America between 1960 and 2000. Our analysis shows a strong correlation between ENSO events and extreme river dynamics in the southeast of Brazil, Carribean South America and parts of the Amazon basin. Furthermore we see a peak in the number of stations showing maximum river discharge all over Brazil during the EN of 1982/83 which has been linked to severe floods in the east of Brazil, parts of Uruguay and Paraguay. However EN events in other years with similar intensity did not evoke floods with such magnitude and therefore the additional drivers of the 1982/83  floods need further investigation. By using machine learning methods to infer data for gauging stations with missing data we were able to extend our data by almost three-fold, revealing a possible heavier and spatially larger impact of the 1982/83 EN on South America's hydrology than indicated in literature.</p>

scholarly journals Improving Skin Lesion Analysis with Generative Adversarial Networks

2020 ◽  
Author(s):  
Alceu Bissoto ◽  
Sandra Avila
Keyword(s):  
Skin Lesion ◽  
State Of The Art ◽  
Synthetic Data ◽  
Clinical Information ◽  
Analysis Data ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Classification Models ◽  
Adversarial Networks ◽  
Lesion Analysis

Melanoma is the most lethal type of skin cancer. Early diagnosis is crucial to increase the survival rate of those patients due to the possibility of metastasis. Automated skin lesion analysis can play an essential role by reaching people that do not have access to a specialist. However, since deep learning became the state-of-the-art for skin lesion analysis, data became a decisive factor in pushing the solutions further. The core objective of this M.Sc. dissertation is to tackle the problems that arise by having limited datasets. In the first part, we use generative adversarial networks to generate synthetic data to augment our classification model’s training datasets to boost performance. Our method generates high-resolution clinically-meaningful skin lesion images, that when compound our classification model’s training dataset, consistently improved the performance in different scenarios, for distinct datasets. We also investigate how our classification models perceived the synthetic samples and how they can aid the model’s generalization. Finally, we investigate a problem that usually arises by having few, relatively small datasets that are thoroughly re-used in the literature: bias. For this, we designed experiments to study how our models’ use data, verifying how it exploits correct (based on medical algorithms), and spurious (based on artifacts introduced during image acquisition) correlations. Disturbingly, even in the absence of any clinical information regarding the lesion being diagnosed, our classification models presented much better performance than chance (even competing with specialists benchmarks), highly suggesting inflated performances.

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