Attention-based Convolutional Neural Network Research Articles

Manifold attention-enhanced multi-domain convolutional network for decoding motor imagery intention

Research in brain-computer interface (BCI), particularly in brain intention decoding, has made significant strides relying on the remarkable capabilities of deep learning (DL). However, many current DL framework-based models struggle to effectively utilize complementary information present in various domains of electroencephalographic (EEG) signals. This limitation may lead to suboptimal performance, particularly exacerbated by the presence of noise and artifacts. Therefore, this study proposed a manifold attention-based multi-domain convolutional neural network, known as MAMCNet, to benefit from complementary strengths across different domains of EEG signals. Specifically, the EEG signals were divided into multi-band data representations based on frequency. Multiple spatiotemporal convolutional blocks were then devised to simultaneously extract the spatiotemporal feature maps from these sub-band components. To enhance the model's robustness, these maps were mapped onto the Riemannian manifold. Additionally, manifold attention was utilized to emphasize salient spatiotemporal feature maps with attention focused on the target. A frequency convolutional block was specifically designed to merge optimized feature maps from different bands, extracting temporospatiofrequency features in EEG signals comprehensively. The experimental results on two datasets demonstrated that the proposed model outperformed the mainstream DL architecture-based model. These results suggest that the proposed model could offer an alternative scheme for facilitating BCI applications.

Intelligent pest forecasting with meteorological data: An explainable deep learning approach

Accurate pest outbreak forecasting is crucial for protecting agricultural yields and environmental health. This study addresses limitations in existing meteorological data-driven approaches by introducing a novel, interpretable deep learning architecture: the Attention-based Long Short-Term Memory Interaction Convolutional Neural Network (ALIC) model. The ALIC model excels at extracting intricate interrelationships within multivariate time series data encompassing both meteorological and historical pest data. The proposed architecture employs a meticulous three-stage processing pipeline. The initial stage, a feature attention layer, pinpoints the most influential forecasting factors within the input data. Subsequently, the feature interaction extraction layer leverages Long Short-Term Memory (LSTM) networks to capture temporal dynamics and a novel Interaction Convolutional Neural Network (ICNN) to extract feature interactions between meteorological and historical pest data. Finally, the output layer generates accurate pest outbreak forecasts. The ALIC model’s effectiveness is rigorously validated using real-world data obtained from observation stations in Huiyang and Shantou, Guangdong Province, China. The model demonstrably outperforms benchmarks, achieving significantly lower forecasting errors across diverse forecast horizons. This finding underscores the ALIC model’s efficacy in capturing intricate relationships between pest outbreaks and meteorological variables. Importantly, our feature importance analysis pinpoints temperature, rainfall as crucial for pest forecasting, bolstering the model’s utility in pest management.

Quality Prediction Modeling for Industrial Processes Using Multiscale Attention-Based Convolutional Neural Network.

Soft sensors have been increasingly applied for quality prediction in complex industrial processes, which often have different scales of topology and highly coupled spatiotemporal features. However, the existing soft sensing models usually face difficulties in extracting the multiscale local spatiotemporal features in multicoupled complex process data and harnessing them to their full potential to improve the prediction performance. Therefore, a multiscale attention-based CNN (MSACNN) is proposed in this article to alleviate such problems. In MSACNN, convolutional kernels of different sizes are first designed in parallel in the convolutional layers, which can generate feature maps containing local spatiotemporal features at different scales. Meanwhile, a channel-wise attention mechanism is designed on the feature maps in parallel to get their attention weights, representing the significance of the local spatiotemporal feature at different scales. The superiority of the proposed MSACNN over the other state-of-the-art methods is validated through the performance evaluation in two real industrial processes.

An efficient convolutional neural network for detecting the crime of stealing in videos

In the current age of technological progress, surveillance cameras are extensively placed in densely populated areas to encourage public safety. However, detecting stealing activities within surveillance footage remains a challenge. The global issue of stealing leads to significant casualties and financial losses annually. There has been limited research on efficient techniques to identify this anomaly. In this manuscript, an efficient multi-scale attention-based convolutional neural network with skill optimization algorithm (MSAC-SOA) is proposed for detecting theft during day and night in surveillance videos. This approach integrates with the enhanced single-shot multibook detector (ESSD) for object detection in video footage, facilitating the precise identification and localization of objects within each frame. Subsequently, the proposed MSAC-SOA effectively detects and classifies instances of stealing. Finally, a theft detection alert message is sent to the control room. This approach is assessed with two datasets: University of Central Florida (UCF) crime dataset for daytime theft detection and real-time video data for nighttime theft detection. The proposed method demonstrates faster computation times compared to previous techniques, highlighting its efficiency and suitability in terms of various merits for real-life videos of diverse scenarios. It exhibits robust performance across various metrics, including 99.88% accuracy, 99.66% precision, 100% recall, 99.89% F1-score, 99.83% area under the ROC curve (AUC), and 99.83% specificity.

Lung Cancer Classification using Optimized Attention-based Convolutional Neural Network with DenseNet-201 Transfer Learning Model on CT image

In this paper, the Lung Cancer Classification using Convolutional Neural Network with DenseNet-201 Transfer Learning model optimized through Namib Beetle Optimization Algorithm on CT image (AtCNN-DenseNet-201 TL-NBOA-CT) is proposed. The input data are gathered from the CT Lung image. In pre-processing section, it removes the noise and enhances input images by Modified Sage-Husa Kalman Filtering (MSHKF).The pre-processed image is given into feature extraction phase. Then, six statistical features including mean, variance, entropy, energy, Average Amplitude, kurtosis are extracted based on Improved Empirical Wavelet Transforms (IEWT). Then, the extracted features are given to the Attention-based Convolutional Neural Network with DenseNet-201Transfer Learning (AtCNN-DenseNet-201 TL) to classify the Cancer and Non-Cancer of the CT image, where batch normalization layer of At CNN eliminated and added by DenseNet-201 layer. Namib Beetle Optimization Algorithm (NBOA) proposed in this work to enhance the AtCNN-DenseNet-201 classifier, which precisely classifies the lung cancer. The proposed AtCNN-DenseNet-201 TL-NBOA-CT method is implemented and the effectiveness is assessed with some performance measures. The proposed AtCNN-DenseNet-201 TL-NBOA-CT method attains 18.30 %, 21.37 % and 23.07 % greater precision, 24.84 %, 16.32 % and 31.36 % greater accuracy and 14.32 %, 21.97 % and 23.38 % greater F1-Score compared with existing techniques like detection and categorization of lung cancer CT pictures utilizing improved deep belief network along Gabor filters (CLC-CT-EDBN), presented categorization of lung cancer CT pictures utilizing a three dimensional deep CNN with multi-layer filter (CLC-CT-3D-DCNN), and novel hybrid deep learning technique for early identification of lung cancer utilizing neural networks (CLC-CT-3D-CNN) respectively.

Attention-based convolutional neural network with multi-modal temporal information fusion for motor imagery EEG decoding

Convolutional neural network (CNN) has been widely applied in motor imagery (MI)-based brain computer interface (BCI) to decode electroencephalography (EEG) signals. However, due to the limited perceptual field of convolutional kernel, CNN only extracts features from local region without considering long-term dependencies for EEG decoding. Apart from long-term dependencies, multi-modal temporal information is equally important for EEG decoding because it can offer a more comprehensive understanding of the temporal dynamics of neural processes. In this paper, we propose a novel deep learning network that combines CNN with self-attention mechanism to encapsulate multi-modal temporal information and global dependencies. The network first extracts multi-modal temporal information from two distinct perspectives: average and variance. A shared self-attention module is then designed to capture global dependencies along these two feature dimensions. We further design a convolutional encoder to explore the relationship between average-pooled and variance-pooled features and fuse them into more discriminative features. Moreover, a data augmentation method called signal segmentation and recombination is proposed to improve the generalization capability of the proposed network. The experimental results on the BCI Competition IV-2a (BCIC-IV-2a) and BCI Competition IV-2b (BCIC-IV-2b) datasets show that our proposed method outperforms the state-of-the-art methods and achieves 4-class average accuracy of 85.03% on the BCIC-IV-2a dataset. The proposed method implies the effectiveness of multi-modal temporal information fusion in attention-based deep learning networks and provides a new perspective for MI-EEG decoding. The code is available at https://github.com/Ma-Xinzhi/EEG-TransNet.

A single-joint multi-task motor imagery EEG signal recognition method based on Empirical Wavelet and Multi‐Kernel Extreme Learning Machine

BackgroundIn the pursuit of finer Brain-Computer Interface commands, research focus has shifted towards classifying EEG signals for multiple tasks. While single-joint multitasking motor imagery provides support, distinguishing between EEG signals from the same joint remains challenging due to their similar brain spatial distribution. New methodWe designed experiments involving three motor imagery tasks—wrist extension, wrist flexion, and wrist abduction—with six participants. Based on this, a single-joint multi-task motor imagery EEG signal recognition method using Empirical Wavelet Decomposition and Multi-Kernel Extreme Learning Machine is proposed. This method employs Empirical Wavelet Decomposition (EWT) for modal decomposition, screening, and reconstruction of raw EEG signals, feature extraction using Common Spatial Patterns (CSP), and classification using Multi-Kernel Extreme Learning Machine (MKELM). ResultsAfter EWT processing, differences in time and frequency characteristics between EEG signals of different classes were enhanced, with the MKELM model achieving an average recognition accuracy of 91.93 %. Comparison with other methods and conclusionsWe compared EWT with Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), Local Mean Decomposition (LMD), and Wavelet Packet Decomposition (WPD). The results showed that the differences between various types of EEG signals processed by EWT were the most pronounced. The MKELM model outperformed traditional machine learning models such as Extreme Learning Machine (ELM), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA) in terms of recognition performance, and also exhibited faster training speeds than deep learning models such as Bayesian Convolutional Neural Network (BCNN) and Attention-based Dual-scale Fusion Convolutional Neural Network (ADFCNN). In summary, the proposed method provides a new approach for achieving finer Brain-Computer Interface commands.

AC-CfC: An attention-based convolutional closed-form continuous-time neural network for raw multi-channel EEG-based emotion recognition

Emotion recognition based on electroencephalogram (EEG) is a critical task in the field of affective brain-computer interfaces. However, due to the non-stationarity and individual variability of EEG, hand-designed features cannot adequately capture the nonlinear and high-dimensional properties of raw EEG. Currently, spatial–temporal models have been verified to effectively capture the spatial–temporal information for EEG. However, these models are characterized by complex structures, large parameter counts, and the need for extensive training data. To overcome these disadvantages, this paper propose an end-to-end spatial–temporal model for emotion recognition based on raw EEG, called attention-based convolutional closed-form continuous-time neural network (AC-CfC). This model employs a channel attention mechanism to weight and encode EEG, capturing channel dependencies. Subsequently, one-dimensional convolutional neural networks and closed-form continuous-time neural networks are used to extract deep spatial–temporal features. Additionally, an adaptive loss-controlling mechanism is designed to enhance the model's decision-making ability between classes that are easily confused. To verify the effectiveness of the proposed model, experiments are conducted on the DEAP and DREAMER datasets. The average accuracies of the proposed model reach to 94.76% and 93.01% for valence and arousal in subject-independent experiments on the DEAP dataset, with an improvement of 11.8% and 8.73% respectively compared with the state-of-art model ACRNN. On the DREAMER dataset, the average accuracies reach to 81.83%, 81.22%, and 80.63% for valence, arousal and dominance, with an improvement of 2.55%, 6.64%, and 6.98% respectively over ACRNN. These results show that our proposed model exhibits better performance than some state-of-art models in the same category.

A novel feature-level fusion scheme with multimodal attention CNN for heart sound classification

Background and objectiveMost of the existing machine learning-based heart sound classification methods achieve limited accuracy. Since they primarily depend on single domain feature information and tend to focus equally on each part of the signal rather than employing a selective attention mechanism. In addition, they fail to exploit convolutional neural network (CNN) - based features with an effective fusion strategy. MethodsIn order to overcome these limitations, a novel multimodal attention convolutional neural network (MACNN) with a feature-level fusion strategy, in which Mel-cepstral domain as well as general frequency domain features are incorporated to increase the diversity of the features, is proposed in this paper. In the proposed method, DilationAttenNet is first utilized to construct attention-based CNN feature extractors and then these feature extractors are jointly optimized in MACNN at the feature-level. The attention mechanism aims to suppress irrelevant information and focus on crucial diverse features extracted from the CNN. ResultsExtensive experiments are carried out to study the efficacy of the feature level fusion in comparison to that with early fusion. The results show that the proposed MACNN method significantly outperforms the state-of-the-art approaches in terms of accuracy and score for the two publicly available Github and Physionet datasets. ConclusionThe findings of our experiments demonstrated the high performance for heart sound classification based on the proposed MACNN, and hence have potential clinical usefulness in the identification of heart diseases. This technique can assist cardiologists and researchers in the design and development of heart sound classification methods.

Dual Hybrid Attention Mechanism-Based U-Net for Building Segmentation in Remote Sensing Images

High-resolution remote sensing images (HRRSI) have important theoretical and practical value in urban planning. However, current segmentation methods often struggle with issues like blurred edges and loss of detailed information due to the intricate backgrounds and rich semantics in high-resolution remote sensing images. To tackle these challenges, this paper proposes an end-to-end attention-based Convolutional Neural Network (CNN) called Double Hybrid Attention U-Net (DHAU-Net). We designed a new Double Hybrid Attention structure consisting of dual-parallel hybrid attention modules to replace the skip connections in U-Net, which can eliminate redundant information interference and enhances the collection and utilization of important shallow features. Comprehensive experiments on the Massachusetts remote sensing building dataset and the Inria aerial image labeling dataset demonstrate that our proposed method achieves effective pixel-level building segmentation in urban remote sensing images by eliminating redundant information interference and making full use of shallow features, and improves the segmentation performance without significant time costs (approximately 15%). The evaluation metrics reveal significant results, with an accuracy rate of 0.9808, precision reaching 0.9300, an F1 score of 0.9112, a mean intersection over union (mIoU) of 0.9088, and a recall rate of 0.8932.

Attention-based 3D convolutional recurrent neural network model for multimodal emotion recognition.

Multimodal emotion recognition has become a hot topic in human-computer interaction and intelligent healthcare fields. However, combining information from different human different modalities for emotion computation is still challenging. In this paper, we propose a three-dimensional convolutional recurrent neural network model (referred to as 3FACRNN network) based on multimodal fusion and attention mechanism. The 3FACRNN network model consists of a visual network and an EEG network. The visual network is composed of a cascaded convolutional neural network-time convolutional network (CNN-TCN). In the EEG network, the 3D feature building module was added to integrate band information, spatial information and temporal information of the EEG signal, and the band attention and self-attention modules were added to the convolutional recurrent neural network (CRNN). The former explores the effect of different frequency bands on network recognition performance, while the latter is to obtain the intrinsic similarity of different EEG samples. To investigate the effect of different frequency bands on the experiment, we obtained the average attention mask for all subjects in different frequency bands. The distribution of the attention masks across the different frequency bands suggests that signals more relevant to human emotions may be active in the high frequency bands γ (31-50 Hz). Finally, we try to use the multi-task loss function Lc to force the approximation of the intermediate feature vectors of the visual and EEG modalities, with the aim of using the knowledge of the visual modalities to improve the performance of the EEG network model. The mean recognition accuracy and standard deviation of the proposed method on the two multimodal sentiment datasets DEAP and MAHNOB-HCI (arousal, valence) were 96.75 ± 1.75, 96.86 ± 1.33; 97.55 ± 1.51, 98.37 ± 1.07, better than those of the state-of-the-art multimodal recognition approaches. The experimental results show that starting from the multimodal information, the facial video frames and electroencephalogram (EEG) signals of the subjects are used as inputs to the emotion recognition network, which can enhance the stability of the emotion network and improve the recognition accuracy of the emotion network. In addition, in future work, we will try to utilize sparse matrix methods and deep convolutional networks to improve the performance of multimodal emotion networks.

Enhancing extractive text summarization using natural language processing with an optimal deep learning model

<abstract> <p>Natural language processing (NLP) performs a vital function in text summarization, a task targeted at refining the crucial information from the massive quantity of textual data. NLP methods allow computers to comprehend and process human language, permitting the development of advanced summarization methods. Text summarization includes the automatic generation of a concise and coherent summary of a specified document or collection of documents. Extracting significant insights from text data is crucial as it provides advanced solutions to end-users and business organizations. Automatic text summarization (ATS) computerizes text summarization by decreasing the initial size of the text without the loss of main data features. Deep learning (DL) approaches exhibited significant performance in abstractive and extractive summarization tasks. This research designed an extractive text summarization using NLP with an optimal DL (ETS-NLPODL) model. The major goal of the ETS-NLPODL technique was to exploit feature selection with a hyperparameter-tuned DL model for summarizing the text. In the ETS-NLPODL technique, an initial step of data preprocessing was involved to convert the input text into a compatible format. Next, a feature extraction process was carried out and the optimal set of features was chosen by the hunger games search optimization (HGSO) algorithm. For text summarization, the ETS-NLPODL model used an attention-based convolutional neural network with a gated recurrent unit (ACNN-GRU) model. Finally, the mountain gazelle optimization (MGO) algorithm was employed for the optimal hyperparameter selection of the ACNN-GRU model. The experimental results of the ETS-NLPODL system were examined under the benchmark dataset. The experimentation outcomes pointed out that the ETS-NLPODL technique gained better performance over other methods concerning diverse performance measures.</p> </abstract>

Acoustic-based LEGO recognition using attention-based convolutional neural networks

Acoustic-based LEGO recognition using attention-based convolutional neural networks

Software Reliability Analysis by Using the Bidirectional Attention Based Zeiler-Fergus Convolutional Neural Network

Software quality assurance relies heavily on software reliability as one of its primary metrics. Numerous studies have been conducted to identify the software reliability. Improved software dependability may be studied using a triangular approach that includes software modeling, measurement, and improvement. Each of these steps is critical to the development of a solid software system. Improved accuracy in calculating dependability is critical to managing the quality of software. It has been discovered that deep learning algorithms are excellent methods of assessing many aspects of software dependability. Software systems contain distinct characteristics that can be addressed using deep learning techniques. In this study, a deep-learning-based bidirectional attention-based Zeiler-Fergus convolutional neural network (BA-ZFCNN) technique has been suggested to assess software dependability. In the beginning, the data were standardized by using the scalable error splash method. This approach was then used to extract the software fault-related characteristics using hypertuned evolutionary salp swarm optimization (HESSO). Finally, the Zeiler-Fergus convolutional neural network based on bidirectional attention (BA-ZFCNN) may be used to assess software dependability. The suggested method is used to forecast how many defects or failures there are in a software product. AR1 software defect data is widely used to test the effectiveness of deep learning and traditional machine learning methods. The experimental results reveal that the proposed methods accuracy (96.7%) is higher than the current techniques accuracy.

Research on workflow recognition for liver rupture repair surgery.

Liver rupture repair surgery serves as one tool to treat liver rupture, especially beneficial for cases of mild liver rupture hemorrhage. Liver rupture can catalyze critical conditions such as hemorrhage and shock. Surgical workflow recognition in liver rupture repair surgery videos presents a significant task aimed at reducing surgical mistakes and enhancing the quality of surgeries conducted by surgeons. A liver rupture repair simulation surgical dataset is proposed in this paper which consists of 45 videos collaboratively completed by nine surgeons. Furthermore, an end-to-end SA-RLNet, a self attention-based recurrent convolutional neural network, is introduced in this paper. The self-attention mechanism is used to automatically identify the importance of input features in various instances and associate the relationships between input features. The accuracy of the surgical phase classification of the SA-RLNet approach is 90.6%. The present study demonstrates that the SA-RLNet approach shows strong generalization capabilities on the dataset. SA-RLNet has proved to be advantageous in capturing subtle variations between surgical phases. The application of surgical workflow recognition has promising feasibility in liver rupture repair surgery.

An Attention-Based Convolutional Recurrent Neural Networks for Scene Text Recognition

An Attention-Based Convolutional Recurrent Neural Networks for Scene Text Recognition

ADFCNN: Attention-Based Dual-Scale Fusion Convolutional Neural Network for Motor Imagery Brain-Computer Interface.

Convolutional neural networks (CNNs) have been successfully applied to motor imagery (MI)-based brain-computer interface (BCI). Nevertheless, single-scale CNN fail to extract abundant information over a wide spectrum from EEG signals, while typical multi-scale CNNs cannot effectively fuse information from different scales with concatenation-based methods. To overcome these challenges, we propose a new scheme equipped with attention-based dual-scale fusion convolutional neural network (ADFCNN), which jointly extracts and fuses EEG spectral and spatial information at different scales. This scheme also provides novel insight through self-attention for effective information fusion from different scales. Specifically, temporal convolutions with two different kernel sizes identify EEG μ and β rhythms, while spatial convolutions at two different scales generate global and detailed spatial information, respectively, and the self-attention mechanism performs feature fusion based on the internal similarity of the concatenated features extracted by the dual-scale CNN. The proposed scheme achieves the superior performance compared with state-of-the-art methods in subject-specific motor imagery recognition on BCI Competition IV dataset 2a, 2b and OpenBMI dataset, with the cross-session average classification accuracies of 79.39% and significant improvements of 9.14% on BCI-IV2a, 87.81% and 7.66% on BCI-IV2b, 65.26% and 7.2% on OpenBMI dataset, and the within-session average classification accuracies of 86.87% and significant improvements of 10.89% on BCI-IV2a, 87.26% and 8.07% on BCI-IV2b, 84.29% and 5.17% on OpenBMI dataset, respectively. What is more, ablation experiments are conducted to investigate the mechanism and demonstrate the effectiveness of the dual-scale joint temporal-spatial CNN and self-attention modules. Visualization is also used to reveal the learning process and feature distribution of the model.

Biotic Stress Management in Soil-Less Agriculture Systems: A Deep Learning Approach for Identification of Leaf Miner Pest Infestation

Leaf miner pests pose a serious threat to the productivity, profitability, and sustainability of soil-less tomato cultivation systems. Early and accurate identification of leaf miner infestation is crucial for timely pest control measures. This study presents an efficient approach using attention-based convolutional neural networks for timely identification of this pest infestation. The proposed approach uses both spatial and channel attention modules to enhance the feature extraction capability of the convolutional neural network. The custom model developed was trained using an image dataset collected from tomatoes grown in a hydroponic environment. The different hyper parameters were tuned to get the optimal model performance. The experimental results show that the proposed attention-based CNN model achieved an overall accuracy of 97.87%, 97.10% precision, 98.53% recall, and 97.81% F1-score. Additionally, the model performance was compared with other pre-trained models viz., AlexNet, VGG16, and VGG19, and was found to outperform these state-of-the-art CNN models due to its improved feature extraction capability. The efficiency of the model underlines its potential to be deployed as part of automated pest monitoring systems in hydroponic environments. This work contributes to the development of computer vision and deep learning-based solutions for precision agriculture applications.

A novel deep learning method for large-scale analysis of bone marrow adiposity using UK Biobank Dixon MRI data

BackgroundBone marrow adipose tissue (BMAT) represents > 10% fat mass in healthy humans and can be measured by magnetic resonance imaging (MRI) as the bone marrow fat fraction (BMFF). Human MRI studies have identified several diseases associated with BMFF but have been relatively small scale. Population-scale studies therefore have huge potential to reveal BMAT’s true clinical relevance. The UK Biobank (UKBB) is undertaking MRI of 100,000 participants, providing the ideal opportunity for such advances. ObjectiveTo establish deep learning for high-throughput multi-site BMFF analysis from UKBB MRI data. Materials and methodsWe studied males and females aged 60–69. Bone marrow (BM) segmentation was automated using a new lightweight attention-based 3D U-Net convolutional neural network that improved segmentation of small structures from large volumetric data. Using manual segmentations from 61–64 subjects, the models were trained to segment four BM regions of interest: the spine (thoracic and lumbar vertebrae), femoral head, total hip and femoral diaphysis. Models were tested using a further 10–12 datasets per region and validated using datasets from 729 UKBB participants. BMFF was then quantified and pathophysiological characteristics assessed, including site- and sex-dependent differences and the relationships with age, BMI, bone mineral density, peripheral adiposity, and osteoporosis. ResultsModel accuracy matched or exceeded that for conventional U-Nets, yielding Dice scores of 91.2% (spine), 94.5% (femoral head), 91.2% (total hip) and 86.6% (femoral diaphysis). One case of severe scoliosis prevented segmentation of the spine, while one case of Non-Hodgkin Lymphoma prevented segmentation of the spine, femoral head and total hip because of T2 signal depletion; however, successful segmentation was not disrupted by any other pathophysiological variables. The resulting BMFF measurements confirmed expected relationships between BMFF and age, sex and bone density, and identified new site- and sex-specific characteristics. ConclusionsWe have established a new deep learning method for accurate segmentation of small structures from large volumetric data, allowing high-throughput multi-site BMFF measurement in the UKBB. Our findings reveal new pathophysiological insights, highlighting the potential of BMFF as a novel clinical biomarker. Applying our method across the full UKBB cohort will help to reveal the impact of BMAT on human health and disease.

Winter wheat yield estimation at the field scale using sentinel-2 data and deep learning

Accurately expressing the spatial pattern of crop yield at a sizeable regional field scale is paramount for precision agriculture. However, the current methodologies consistently face numerous challenges, comprising inadequate yield samples and algorithmic tendencies to underestimate higher yield values and overestimate lower ones. Data-driven deep learning algorithms effectively transform remotely sensed data into high-dimensional feature representations. Developing a cost-efficient model that minimizes the impact of insufficient field samples while maximizing the representation of crop yield differences on plots is worthwhile. The study developed an ACNN (Attention-based One-dimensional Convolutional Neural Network) model to efficiently extract and optimize the spatiotemporal features of winter wheat yield from Sentinel-2 MSI Level-2A data while relatively reducing the model's reliance on training samples. Winter wheat yield at both county and field scales in Henan Province was estimated utilizing Random Forest (RF), Convolutional Neural Network (CNN), and the ACNN models. The proposed ACNN model effectively identified key phenological stages and discerned noteworthy spectral bands essential for field-scale winter wheat yield estimation. It underscores the heading stage as a pivotal period influencing yield formation, emphasizing the indispensable contributions of the B8 (835.1 nm) and B12 (2202.4 nm) spectral bands in yield estimation. Moreover, this model adeptly overcame the challenge of underestimating higher values and overestimating lower values in crop yield estimation while showcasing robust generalization ability. The study presents a practical method for field-scale crop yield estimation over a large area with insufficient yield samples.