MHW-PD: A robust rice panicles counting algorithm based on deep learning and multi-scale hybrid window

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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Panicle-SEG: a robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization

Plant Methods ◽

10.1186/s13007-017-0254-7 ◽

2017 ◽

Vol 13 (1) ◽

Cited By ~ 52

Author(s):

Xiong Xiong ◽

Lingfeng Duan ◽

Lingbo Liu ◽

Haifu Tu ◽

Peng Yang ◽

...

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Segmentation Method ◽

Robust Image ◽

Rice Panicles

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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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Automated Counting Grains on the Rice Panicle Based on Deep Learning Method

Sensors ◽

10.3390/s21010281 ◽

2021 ◽

Vol 21 (1) ◽

pp. 281

Author(s):

Ruoling Deng ◽

Ming Tao ◽

Xunan Huang ◽

Kemoh Bangura ◽

Qian Jiang ◽

...

Keyword(s):

Deep Learning ◽

Model Performance ◽

Rice Breeding ◽

Grain Number ◽

Lighting Condition ◽

Detection Model ◽

Grain Moisture ◽

Feature Pyramid ◽

Moisture Conditions ◽

Rice Panicles

Grain number per rice panicle, which directly determines grain yield, is an important agronomic trait for rice breeding and yield-related research. However, manually counting grains of rice per panicle is time-consuming, laborious, and error-prone. In this research, a grain detection model was proposed to automatically recognize and count grains on primary branches of a rice panicle. The model used image analysis based on deep learning convolutional neural network (CNN), by integrating the feature pyramid network (FPN) into the faster R-CNN network. The performance of the grain detection model was compared to that of the original faster R-CNN model and the SSD model, and it was found that the grain detection model was more reliable and accurate. The accuracy of the grain detection model was not affected by the lighting condition in which images of rice primary branches were taken. The model worked well for all rice branches with various numbers of grains. Through applying the grain detection model to images of fresh and dry branches, it was found that the model performance was not affected by the grain moisture conditions. The overall accuracy of the grain detection model was 99.4%. Results demonstrated that the model was accurate, reliable, and suitable for detecting grains of rice panicles with various conditions.

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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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