Multi-scale deep learning for estimating horizontal velocity fields on the solar surface

Analyzing the surface and bedrock locations in radar imagery enables the computation of ice sheet thickness, which is important for the study of ice sheets, their volume and how they may contribute to global climate change. However, the traditional handcrafted methods cannot quickly provide quantitative, objective and reliable extraction of information from radargrams. Most traditional handcrafted methods, designed to detect ice-surface and ice-bed layers from ice sheet radargrams, require complex human involvement and are difficult to apply to large datasets, while deep learning methods can obtain better results in a generalized way. In this study, an end-to-end multi-scale attention network (MsANet) is proposed to realize the estimation and reconstruction of layers in sequences of ice sheet radar tomographic images. First, we use an improved 3D convolutional network, C3D-M, whose first full connection layer is replaced by a convolution unit to better maintain the spatial relativity of ice layer features, as the backbone. Then, an adjustable multi-scale module uses different scale filters to learn scale information to enhance the feature extraction capabilities of the network. Finally, an attention module extended to 3D space removes a redundant bottleneck unit to better fuse and refine ice layer features. Radar sequential images collected by the Center of Remote Sensing of Ice Sheets in 2014 are used as training and testing data. Compared with state-of-the-art deep learning methods, the MsANet shows a 10% reduction (2.14 pixels) on the measurement of average mean absolute column-wise error for detecting the ice-surface and ice-bottom layers, runs faster and uses approximately 12 million fewer parameters.

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MMNet: A multi-scale deep learning network for the left ventricular segmentation of cardiac MRI images

Applied Intelligence ◽

10.1007/s10489-021-02720-9 ◽

2021 ◽

Author(s):

Ziyue Wang ◽

Yanjun Peng ◽

Dapeng Li ◽

Yanfei Guo ◽

Bin Zhang

Keyword(s):

Deep Learning ◽

Cardiac Mri ◽

Left Ventricular ◽

Learning Network ◽

Multi Scale ◽

Deep Learning Network

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Hyperspectral Image Classification Based on Multi-Scale Residual Network with Attention Mechanism

Remote Sensing ◽

10.3390/rs13030335 ◽

2021 ◽

Vol 13 (3) ◽

pp. 335

Author(s):

Yuhao Qing ◽

Wenyi Liu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Image Classification ◽

Classification Accuracy ◽

Hyperspectral Image ◽

Principal Component ◽

Hyperspectral Image Classification ◽

Deep Network ◽

Multi Scale

In recent years, image classification on hyperspectral imagery utilizing deep learning algorithms has attained good results. Thus, spurred by that finding and to further improve the deep learning classification accuracy, we propose a multi-scale residual convolutional neural network model fused with an efficient channel attention network (MRA-NET) that is appropriate for hyperspectral image classification. The suggested technique comprises a multi-staged architecture, where initially the spectral information of the hyperspectral image is reduced into a two-dimensional tensor, utilizing a principal component analysis (PCA) scheme. Then, the constructed low-dimensional image is input to our proposed ECA-NET deep network, which exploits the advantages of its core components, i.e., multi-scale residual structure and attention mechanisms. We evaluate the performance of the proposed MRA-NET on three public available hyperspectral datasets and demonstrate that, overall, the classification accuracy of our method is 99.82 %, 99.81%, and 99.37, respectively, which is higher compared to the corresponding accuracy of current networks such as 3D convolutional neural network (CNN), three-dimensional residual convolution structure (RES-3D-CNN), and space–spectrum joint deep network (SSRN).

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A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study

Therapeutic Advances in Medical Oncology ◽

10.1177/1758835920971416 ◽

2020 ◽

Vol 12 ◽

pp. 175883592097141

Author(s):

Fan Zhang ◽

Lian-Zhen Zhong ◽

Xun Zhao ◽

Di Dong ◽

Ji-Jin Yao ◽

...

Keyword(s):

Deep Learning ◽

Magnetic Resonance ◽

Nasopharyngeal Carcinoma ◽

Treatment Failure ◽

Digital Pathology ◽

Imaging Features ◽

Multi Scale ◽

External Test ◽

Whole Slide Images ◽

Independent Cohort

Background: To explore the prognostic value of radiomics-based and digital pathology-based imaging biomarkers from macroscopic magnetic resonance imaging (MRI) and microscopic whole-slide images for patients with nasopharyngeal carcinoma (NPC). Methods: We recruited 220 NPC patients and divided them into training ( n = 132), internal test ( n = 44), and external test ( n = 44) cohorts. The primary endpoint was failure-free survival (FFS). Radiomic features were extracted from pretreatment MRI and selected and integrated into a radiomic signature. The histopathological signature was extracted from whole-slide images of biopsy specimens using an end-to-end deep-learning method. Incorporating two signatures and independent clinical factors, a multi-scale nomogram was constructed. We also tested the correlation between the key imaging features and genetic alternations in an independent cohort of 16 patients (biological test cohort). Results: Both radiomic and histopathologic signatures presented significant associations with treatment failure in the three cohorts (C-index: 0.689–0.779, all p < 0.050). The multi-scale nomogram showed a consistent significant improvement for predicting treatment failure compared with the clinical model in the training (C-index: 0.817 versus 0.730, p < 0.050), internal test (C-index: 0.828 versus 0.602, p < 0.050) and external test (C-index: 0.834 versus 0.679, p < 0.050) cohorts. Furthermore, patients were stratified successfully into two groups with distinguishable prognosis (log-rank p < 0.0010) using our nomogram. We also found that two texture features were related to the genetic alternations of chromatin remodeling pathways in another independent cohort. Conclusion: The multi-scale imaging features showed a complementary value in prognostic prediction and may improve individualized treatment in NPC.

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Multi-Scale Distributed Representation for Deep Learning and its Application to b-Jet Tagging

Journal of the Korean Physical Society ◽

10.3938/jkps.72.1292 ◽

2018 ◽

Vol 72 (11) ◽

pp. 1292-1300

Author(s):

Jason Sang Hun Lee ◽

Inkyu Park ◽

Sangnam Park

Keyword(s):

Deep Learning ◽

Distributed Representation ◽

Multi Scale

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Localization of the Epileptic Focus using Multi-scale Deep Learning

10.1145/3484377.3484380 ◽

2021 ◽

Author(s):

Rui Zhang ◽

YongJin Tang ◽

Rui Yan ◽

Tie Cai ◽

Hao Xu

Keyword(s):

Deep Learning ◽

Epileptic Focus ◽

Multi Scale

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MUFFIN: multi-scale feature fusion for drug–drug interaction prediction

Bioinformatics ◽

10.1093/bioinformatics/btab169 ◽

2021 ◽

Author(s):

Yujie Chen ◽

Tengfei Ma ◽

Xixi Yang ◽

Jianmin Wang ◽

Bosheng Song ◽

...

Keyword(s):

Molecular Structure ◽

Deep Learning ◽

Medical Information ◽

Feature Fusion ◽

Molecular Graph ◽

Knowledge Graph ◽

Sequence Information ◽

Learning Models ◽

Scale Feature ◽

Multi Scale

Abstract Motivation Adverse drug–drug interactions (DDIs) are crucial for drug research and mainly cause morbidity and mortality. Thus, the identification of potential DDIs is essential for doctors, patients and the society. Existing traditional machine learning models rely heavily on handcraft features and lack generalization. Recently, the deep learning approaches that can automatically learn drug features from the molecular graph or drug-related network have improved the ability of computational models to predict unknown DDIs. However, previous works utilized large labeled data and merely considered the structure or sequence information of drugs without considering the relations or topological information between drug and other biomedical objects (e.g. gene, disease and pathway), or considered knowledge graph (KG) without considering the information from the drug molecular structure. Results Accordingly, to effectively explore the joint effect of drug molecular structure and semantic information of drugs in knowledge graph for DDI prediction, we propose a multi-scale feature fusion deep learning model named MUFFIN. MUFFIN can jointly learn the drug representation based on both the drug-self structure information and the KG with rich bio-medical information. In MUFFIN, we designed a bi-level cross strategy that includes cross- and scalar-level components to fuse multi-modal features well. MUFFIN can alleviate the restriction of limited labeled data on deep learning models by crossing the features learned from large-scale KG and drug molecular graph. We evaluated our approach on three datasets and three different tasks including binary-class, multi-class and multi-label DDI prediction tasks. The results showed that MUFFIN outperformed other state-of-the-art baselines. Availability and implementation The source code and data are available at https://github.com/xzenglab/MUFFIN.

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Comparison of solar horizontal velocity fields from SDO/HMI and Hinode data

Astronomy and Astrophysics ◽

10.1051/0004-6361/201220867 ◽

2013 ◽

Vol 552 ◽

pp. A113 ◽

Cited By ~ 12

Author(s):

Th. Roudier ◽

M. Rieutord ◽

V. Prat ◽

J. M. Malherbe ◽

N. Renon ◽

...

Keyword(s):

Velocity Fields ◽

Horizontal Velocity

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A Novel Parallel Auto-Encoder Framework for Multi-Scale Data in Civil Structural Health Monitoring

Algorithms ◽

10.3390/a11080112 ◽

2018 ◽

Vol 11 (8) ◽

pp. 112 ◽

Cited By ~ 4

Author(s):

Ruhua Wang ◽

Ling Li ◽

Jun Li

Keyword(s):

Deep Learning ◽

Dimension Reduction ◽

Structural Damage ◽

Natural Frequencies ◽

Steel Structure ◽

Mode Shapes ◽

Multi Scale ◽

Training Samples ◽

Sparsity Constraint ◽

Deep Learning Network

In this paper, damage detection/identification for a seven-storey steel structure is investigated via using the vibration signals and deep learning techniques. Vibration characteristics, such as natural frequencies and mode shapes are captured and utilized as input for a deep learning network while the output vector represents the structural damage associated with locations. The deep auto-encoder with sparsity constraint is used for effective feature extraction for different types of signals and another deep auto-encoder is used to learn the relationship of different signals for final regression. The existing SAF model in a recent research study for the same problem processed all signals in one serial auto-encoder model. That kind of models have the following difficulties: (1) the natural frequencies and mode shapes are in different magnitude scales and it is not logical to normalize them in the same scale in building the models with training samples; (2) some frequencies and mode shapes may not be related to each other and it is not fair to use them for dimension reduction together. To tackle the above-mentioned problems for the multi-scale dataset in SHM, a novel parallel auto-encoder framework (Para-AF) is proposed in this paper. It processes the frequency signals and mode shapes separately for feature selection via dimension reduction and then combine these features together in relationship learning for regression. Furthermore, we introduce sparsity constraint in model reduction stage for performance improvement. Two experiments are conducted on performance evaluation and our results show the significant advantages of the proposed model in comparison with the existing approaches.

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