Pixel-level Road Pavement Defects Segmentation Based on Various Loss Functions

Augustauskas, Rytis; Lipnickas, Arūnas

doi:10.1109/idaacs53288.2021.9660845

Cited by 2 publications

(2 citation statements)

References 30 publications

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“…Smaller networks like ours, or as in the one applied for automatic diagnosis of 12-lead ECGs [ 42 ], outperform their convolutional-blocks-only counterparts when enhanced with custom blocks such as residual connections, squeeze-and-excitation, atrous spatial pooling, or case-specific loss functions [ 32 , 33 ]. Our strategy involved residual blocks with a predominant focus on an original loss function.…”

Section: Discussionmentioning

confidence: 99%

“…First, complex CNNs employed for image-based applications are likely an overengineered solution for our problem. Second, smaller CNN architectures enhanced with residual connections and case-specific loss functions can outperform architectures based on regular convolutional blocks [ 32 , 33 ]. Third, lightweight and low-complexity models are preferable for deployment in devices with hardware and computational constraints, such as consumer healthcare devices.…”

Section: Deep-learning-based Approach For Qrs-t Angle Estimationmentioning

confidence: 99%

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Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs

Rodrigues

Augustauskas

Lukoševičius

et al. 2022

Sensors

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The spatial QRS-T angle is a promising health indicator for risk stratification of sudden cardiac death (SCD). Thus far, the angle is estimated solely from 12-lead electrocardiogram (ECG) systems uncomfortable for ambulatory monitoring. Methods to estimate QRS-T angles from reduced-lead ECGs registered with consumer healthcare devices would, therefore, facilitate ambulatory monitoring. (1) Objective: Develop a method to estimate spatial QRS-T angles from reduced-lead ECGs. (2) Approach: We designed a deep learning model to locate the QRS and T wave vectors necessary for computing the QRS-T angle. We implemented an original loss function to guide the model in the 3D space to search for each vector’s coordinates. A gradual reduction of ECG leads from the largest publicly available dataset of clinical 12-lead ECG recordings (PTB-XL) is used for training and validation. (3) Results: The spatial QRS-T angle can be estimated from leads {I, II, aVF, V2} with sufficient accuracy (absolute mean and median errors of 11.4° and 7.3°) for detecting abnormal angles without sacrificing patient comfortability. (4) Significance: Our model could enable ambulatory monitoring of spatial QRS-T angles using patch- or textile-based ECG devices. Populations at risk of SCD, like chronic cardiac and kidney disease patients, might benefit from this technology.

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Section: Discussionmentioning

confidence: 99%

Section: Deep-learning-based Approach For Qrs-t Angle Estimationmentioning

confidence: 99%

Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs

Rodrigues

Augustauskas

Lukoševičius

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

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A Bidirectional LSTM-RNN and GRU Method to Exon Prediction Using Splice-Site Mapping

Canatalay

Uçan

2022

Applied Sciences

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Deep Learning techniques (DL) significantly improved the accuracy of predictions and classifications of deoxyribonucleic acid (DNA). On the other hand, identifying and predicting splice sites in eukaryotes is difficult due to many erroneous discoveries. To address this issue, we propose a deep learning model for recognizing and anticipating splice sites in eukaryotic DNA sequences based on a bidirectional Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) and Gated recurrent unit (GRU). The non-coding introns of the gene are spliced out, and the coding exons are joined during the splicing of the original mRNA transcript. This bidirectional LSTM-RNN-GRU model incorporates intron features in order of their length constraints, beginning with splice site donor (GT) and ending with splice site acceptor (AG). The performance of the model improves as the number of training epochs grows. The best level of accuracy for this model is 96.1 percent.

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Pixel-level Road Pavement Defects Segmentation Based on Various Loss Functions

Cited by 2 publications

References 30 publications

Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs

Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs

A Bidirectional LSTM-RNN and GRU Method to Exon Prediction Using Splice-Site Mapping

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