scholarly journals Benchmarking Deep Learning Models and Automated Model Design for COVID-19 Detection with Chest CT Scans

2020 ◽  
Author(s):  
Xin He ◽  
Shihao Wang ◽  
Shaohuai Shi ◽  
Xiaowen Chu ◽  
Jiangping Tang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Health Care Professionals ◽  
Data Augmentation ◽  
Model Performance ◽  
Chest Ct ◽  
Ct Scans ◽  
Data Sets ◽  
Learning Models ◽  
3D Cnn ◽  
Novel Coronavirus

AbstractCOVID-19 pandemic has spread all over the world for months. As its transmissibility and high pathogenicity seriously threaten people’s lives, the accurate and fast detection of the COVID-19 infection is crucial. Although many recent studies have shown that deep learning based solutions can help detect COVID-19 based on chest CT scans, there lacks a consistent and systematic comparison and evaluation on these techniques. In this paper, we first build a clean and segmented CT dataset called Clean-CC-CCII by fixing the errors and removing some noises in a large CT scan dataset CC-CCII with three classes: novel coronavirus pneumonia (NCP), common pneumonia (CP), and normal controls (Normal). After cleaning, our dataset consists of a total of 340,190 slices of 3,993 scans from 2,698 patients. Then we benchmark and compare the performance of a series of state-of-the-art (SOTA) 3D and 2D convolutional neural networks (CNNs). The results show that 3D CNNs outperform 2D CNNs in general. With extensive effort of hyperparameter tuning, we find that the 3D CNN model DenseNet3D121 achieves the highest accuracy of 88.63% (F1-score is 88.14% and AUC is 0.940), and another 3D CNN model ResNet3D34 achieves the best AUC of 0.959 (accuracy is 87.83% and F1-score is 86.04%). We further demonstrate that the mixup data augmentation technique can largely improve the model performance. At last, we design an automated deep learning methodology to generate a lightweight deep learning model MNas3DNet41 that achieves an accuracy of 87.14%, F1-score of 87.25%, and AUC of 0.957, which are on par with the best models made by AI experts. The automated deep learning design is a promising methodology that can help health-care professionals develop effective deep learning models using their private data sets. Our Clean-CC-CCII dataset and source code are available at:https://github.com/arthursdays/HKBU HPML COVID-19.

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

scholarly journals A Deep Learning Approach For Improved Segmentation Of Lesions Related To Covid-19 Chest CT Scans

2021 ◽  
Author(s):  
Vlad Vasilescu ◽  
Ana Neacsu ◽  
Emilie Chouzenoux ◽  
Jean-Christophe Pesquet ◽  
Corneliu Burileanu
Keyword(s):  
Deep Learning ◽  
Chest Ct ◽  
Ct Scans ◽  
Learning Approach

scholarly journals Reviewing the relationship between machines and radiology: the application of artificial intelligence

2021 ◽  
Vol 10 (2) ◽  
pp. 205846012199029
Author(s):  
Rani Ahmad
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Health Care Professionals ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Health Science ◽  
Computer Algorithms ◽  
Learning Models ◽  
Specificity And Sensitivity

Background The scope and productivity of artificial intelligence applications in health science and medicine, particularly in medical imaging, are rapidly progressing, with relatively recent developments in big data and deep learning and increasingly powerful computer algorithms. Accordingly, there are a number of opportunities and challenges for the radiological community. Purpose To provide review on the challenges and barriers experienced in diagnostic radiology on the basis of the key clinical applications of machine learning techniques. Material and Methods Studies published in 2010–2019 were selected that report on the efficacy of machine learning models. A single contingency table was selected for each study to report the highest accuracy of radiology professionals and machine learning algorithms, and a meta-analysis of studies was conducted based on contingency tables. Results The specificity for all the deep learning models ranged from 39% to 100%, whereas sensitivity ranged from 85% to 100%. The pooled sensitivity and specificity were 89% and 85% for the deep learning algorithms for detecting abnormalities compared to 75% and 91% for radiology experts, respectively. The pooled specificity and sensitivity for comparison between radiology professionals and deep learning algorithms were 91% and 81% for deep learning models and 85% and 73% for radiology professionals (p < 0.000), respectively. The pooled sensitivity detection was 82% for health-care professionals and 83% for deep learning algorithms (p < 0.005). Conclusion Radiomic information extracted through machine learning programs form images that may not be discernible through visual examination, thus may improve the prognostic and diagnostic value of data sets.

scholarly journals COVID-19 identification from volumetric chest CT scans using a progressively resized 3D-CNN incorporating segmentation, augmentation, and class-rebalancing

2021 ◽  
pp. 100709
Author(s):  
Md. Kamrul Hasan ◽  
Md. Tasnim Jawad ◽  
Kazi Nasim Imtiaz Hasan ◽  
Sajal Basak Partha ◽  
Md. Masum Al Masba ◽  
...  
Keyword(s):  
Chest Ct ◽  
Ct Scans ◽  
3D Cnn

scholarly journals KeyNet: An Asymmetric Key-Style Framework for Watermarking Deep Learning Models

2021 ◽  
Vol 11 (3) ◽  
pp. 999
Author(s):  
Najeeb Moharram Jebreel ◽  
Josep Domingo-Ferrer ◽  
David Sánchez ◽  
Alberto Blanco-Justicia
Keyword(s):  
Deep Learning ◽  
Intellectual Property ◽  
High Fidelity ◽  
Data Sets ◽  
Learning Models ◽  
Robust Watermarking ◽  
Empirical Results ◽  
Private Key ◽  
Extensive Evaluation ◽  
Original Classification

Many organizations devote significant resources to building high-fidelity deep learning (DL) models. Therefore, they have a great interest in making sure the models they have trained are not appropriated by others. Embedding watermarks (WMs) in DL models is a useful means to protect the intellectual property (IP) of their owners. In this paper, we propose KeyNet, a novel watermarking framework that satisfies the main requirements for an effective and robust watermarking. In KeyNet, any sample in a WM carrier set can take more than one label based on where the owner signs it. The signature is the hashed value of the owner’s information and her model. We leverage multi-task learning (MTL) to learn the original classification task and the watermarking task together. Another model (called the private model) is added to the original one, so that it acts as a private key. The two models are trained together to embed the WM while preserving the accuracy of the original task. To extract a WM from a marked model, we pass the predictions of the marked model on a signed sample to the private model. Then, the private model can provide the position of the signature. We perform an extensive evaluation of KeyNet’s performance on the CIFAR10 and FMNIST5 data sets and prove its effectiveness and robustness. Empirical results show that KeyNet preserves the utility of the original task and embeds a robust WM.

scholarly journals Factors determining generalization in deep learning models for scoring COVID-CT images

2021 ◽  
Vol 18 (6) ◽  
pp. 9264-9293
Author(s):  
Michael James Horry ◽  
◽  
Subrata Chakraborty ◽  
Biswajeet Pradhan ◽  
Maryam Fallahpoor ◽  
...  
Keyword(s):  
Deep Learning ◽  
Predictive Accuracy ◽  
Gabor Filter ◽  
Predictive Ability ◽  
Histogram Equalization ◽  
Data Sets ◽  
Lung Involvement ◽  
Learning Models ◽  
Key Factors ◽  
Independent Dataset

<abstract> <p>The COVID-19 pandemic has inspired unprecedented data collection and computer vision modelling efforts worldwide, focused on the diagnosis of COVID-19 from medical images. However, these models have found limited, if any, clinical application due in part to unproven generalization to data sets beyond their source training corpus. This study investigates the generalizability of deep learning models using publicly available COVID-19 Computed Tomography data through cross dataset validation. The predictive ability of these models for COVID-19 severity is assessed using an independent dataset that is stratified for COVID-19 lung involvement. Each inter-dataset study is performed using histogram equalization, and contrast limited adaptive histogram equalization with and without a learning Gabor filter. We show that under certain conditions, deep learning models can generalize well to an external dataset with F1 scores up to 86%. The best performing model shows predictive accuracy of between 75% and 96% for lung involvement scoring against an external expertly stratified dataset. From these results we identify key factors promoting deep learning generalization, being primarily the uniform acquisition of training images, and secondly diversity in CT slice position.</p> </abstract>

scholarly journals Assessing the Performance of Deep Learning Algorithms for Short-Term Surface Water Quality Prediction

2021 ◽  
Vol 13 (19) ◽  
pp. 10690
Author(s):  
Heelak Choi ◽  
Sang-Ik Suh ◽  
Su-Hee Kim ◽  
Eun Jin Han ◽  
Seo Jin Ki
Keyword(s):  
Water Quality ◽  
Deep Learning ◽  
Surface Water ◽  
Learning Algorithms ◽  
Arima Model ◽  
Surface Water Quality ◽  
Percentage Error ◽  
Data Sets ◽  
Learning Models ◽  
Dependent Variables

This study aimed to investigate the applicability of deep learning algorithms to (monthly) surface water quality forecasting. A comparison was made between the performance of an autoregressive integrated moving average (ARIMA) model and four deep learning models. All prediction algorithms, except for the ARIMA model working on a single variable, were tested with univariate inputs consisting of one of two dependent variables as well as multivariate inputs containing both dependent and independent variables. We found that deep learning models (6.31–18.78%, in terms of the mean absolute percentage error) showed better performance than the ARIMA model (27.32–404.54%) in univariate data sets, regardless of dependent variables. However, the accuracy of prediction was not improved for all dependent variables in the presence of other associated water quality variables. In addition, changes in the number of input variables, sliding window size (i.e., input and output time steps), and relevant variables (e.g., meteorological and discharge parameters) resulted in wide variation of the predictive accuracy of deep learning models, reaching as high as 377.97%. Therefore, a refined search identifying the optimal values on such influencing factors is recommended to achieve the best performance of any deep learning model in given multivariate data sets.

Data Augmentation for Improving Deep Learning Models in Building Inspections or Postdisaster Evaluation

2021 ◽  
Vol 35 (4) ◽  
Author(s):  
Samuel Leach ◽  
Yunhe Xue ◽  
Rahul Sridhar ◽  
Stephanie Paal ◽  
Zhangyang Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Learning Models

scholarly journals deepSIP: linking Type Ia supernova spectra to photometric quantities with deep learning

2020 ◽  
Vol 496 (3) ◽  
pp. 3553-3571
Author(s):  
Benjamin E Stahl ◽  
Jorge Martínez-Palomera ◽  
WeiKang Zheng ◽  
Thomas de Jaeger ◽  
Alexei V Filippenko ◽  
...  
Keyword(s):  
Deep Learning ◽  
Light Curve ◽  
Data Augmentation ◽  
Data Sets ◽  
Curve Shape ◽  
Learning Standards ◽  
Type Ia Supernova ◽  
Type Ia ◽  
Augmentation Strategy ◽  
Hyperparameter Selection

ABSTRACT We present deepSIP (deep learning of Supernova Ia Parameters), a software package for measuring the phase and – for the first time using deep learning – the light-curve shape of a Type Ia supernova (SN Ia) from an optical spectrum. At its core, deepSIP consists of three convolutional neural networks trained on a substantial fraction of all publicly available low-redshift SN Ia optical spectra, on to which we have carefully coupled photometrically derived quantities. We describe the accumulation of our spectroscopic and photometric data sets, the cuts taken to ensure quality, and our standardized technique for fitting light curves. These considerations yield a compilation of 2754 spectra with photometrically characterized phases and light-curve shapes. Though such a sample is significant in the SN community, it is small by deep-learning standards where networks routinely have millions or even billions of free parameters. We therefore introduce a data-augmentation strategy that meaningfully increases the size of the subset we allocate for training while prioritizing model robustness and telescope agnosticism. We demonstrate the effectiveness of our models by deploying them on a sample unseen during training and hyperparameter selection, finding that Model I identifies spectra that have a phase between −10 and 18 d and light-curve shape, parametrized by Δm15, between 0.85 and 1.55 mag with an accuracy of 94.6 per cent. For those spectra that do fall within the aforementioned region in phase–Δm15 space, Model II predicts phases with a root-mean-square error (RMSE) of 1.00 d and Model III predicts Δm15 values with an RMSE of 0.068 mag.

scholarly journals AI aiding in diagnosing, tracking recovery of COVID-19 using deep learning on Chest CT scans

2020 ◽  
Author(s):  
Maheshwar Kuchana ◽  
Amritesh Srivastava ◽  
Ronald Das ◽  
Justin Mathew ◽  
Atul Mishra ◽  
...  
Keyword(s):  
Deep Learning ◽  
Chest Ct ◽  
Ct Scans
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