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Efficient hand segmentation for rehabilitation tasks using a convolution neural network with attention

EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期234卷

作者： Dutta, H. Pallab Jyoti Bhuyan, M. K. Neog, Debanga Raj Macdorman, Karl Fredric Laskar, Rabul Hussain Indian Inst Technol Dept Elect & Elect Engn Gauhati 781039 Assam India Indian Inst Technol Mehta Family Sch Data Sci & Artificial Intelligenc Gauhati 781039 Assam India Indiana Univ Luddy Sch Informat Comp & Engn Indianapolis IN 46202 USA Natl Inst Technol Silchar Dept Elect & Commun Engn Silchar 788010 Assam India

We designed an interface to support hand rehabilitation tasks to restore hand function and relieve discomfort. The interface requires accurate hand segmentation, which is impeded by background clutter, occlusion, and variations in illumination. To overcome these challenges, we propose a novel encoder-decoder that segments the hand by encoding spatial and channel correlations using two attention blocks. This approach requires much less computation than benchmark self-attention mechanisms. Moreover, a novel loss function optimizes the model to resolve class imbalance, ensure boundary smoothness, and retain the hand's shape. The quantitative and qualitative results show the model's ability to segment the hands. It performed exceptionally well for images with different hand poses and orientations, the presence of a human face, background clutter, specularity, and variations in illumination. The model attained an F1-score of 97.3% for the Ouhands and 99.3% for the HGR dataset, higher than baseline models, with faster inference times. Furthermore, the model could generalize hand segmentation to multiple hands and unseen environments. Its segmentation precision enabled the development of the hand rehabilitation interface, which guided users to perform hand exercises. For five weeks, patients steadily improved hand function while using the interface.

关键词： Channel attention Efficient attention mechanism encoder-decoder Hand rehabilitation Hand segmentation Spatial attention

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Fast MRI reconstruction using StrainNet with dual-domain loss on spatial and frequency spaces

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INTELLIGENT SYSTEMS WITH APPLICATIONS 2023年 18卷

作者： Kusakunniran, Worapan Karnjanapreechakorn, Sarattha Siriapisith, Thanongchai Saiviroonporn, Pairash Mahidol Univ Fac Informat & Commun Technol 999 Phuttamonthon 4 Rd Nakhon Pathom 73170 Thailand Mahidol Univ Siriraj Hosp Dept Radiol Fac Med 2 Wanglang Rd Bangkok 10700 Thailand

One of the main challenges to obtain a high throughput in the MRI process is a slow signal acquisition. This process could be improved using a parallel imaging technique, where fewer raw data with multiple radio frequency (RF) coils are acquired simultaneously to reconstruct a final MR image. Nowadays, all multi-coil MRI machines have a parallel imaging technique for the image reconstruction. However, the parallel imaging still cannot accelerate sufficiently to reduce the overall acquisition time. In another way, this paper proposes a solution relying on a deep convolution neural network (CNN) to generate high-quality reconstruction MR images with higher acceleration factors. The proposed method, called StrainNet, performs the reconstructions by encoding the under-sampled data (i.e., for the speeding up process) into high-level features. Then the important part of the network, called Strainer, is applied to discard irrelevance information, and decodes remaining features back to reconstruct MR images. The proposed network could be trained end-to-end with a newly presented loss function, Dual-Domain Loss (DDL), combining both spatial and frequency losses. The experimental results are based on the fastMRI dataset and show that StrainNet outperforms the competing methods for both 4- and 8-fold accelerations.

关键词： Fast MRI Image reconstruction encoder-decoder CNN Frequency domain

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Keyword-guided abstractive code summarization via incorporating structural and contextual information

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INFORMATION AND SOFTWARE TECHNOLOGY 2022年第0期150卷

作者： Cheng, Wuyan Hu, Po Wei, Shaozhi Mo, Ran Cent China Normal Univ Hubei Prov Key Lab Artificial Intelligence & Smart Wuhan Hubei Peoples R China Cent China Normal Univ Sch Comp Wuhan Hubei Peoples R China Cent China Normal Univ Natl Language Resources Monitoring & Res Ctr Netwo Wuhan Hubei Peoples R China

Context: Source code summarization is a crucial yet far from settled task for describing structured code snippets in natural language. High-quality code summaries could effectively facilitate program comprehension and software maintenance. A good code summary is supposed to have the following characteristics: complete information, correct meaning, and consistent description. In recent years, numerous approaches have been proposed for code summarization, but it is still very challenging for developers to automatically learn the complex semantics from the source code and generate complete, correct and consistent code summaries. Objective: In this paper, we propose KGCodeSum, a novel keyword-guided abstractive code summarization approach that incorporates structural and contextual ***: To improve summaries' quality, we leverage both the structural information embedded in code itself and the contextual information from related code snippets. Meanwhile, we make use of keywords to guide summaries' generation to guarantee the code summaries contain key information. Finally, we propose a new dynamic vocabulary strategy which can effectively resolve the UNK problems in code ***: Through our evaluation on the large-scale benchmark datasets with 2.1 million java method-comment pairs and 1.1 million C/C++ function-summary pairs, We have observed that our approach could generate better code summaries than existing state-of-the-art approaches in terms of completeness, correctness and consistency. In addition, we also find that incorporating the dynamic vocabulary strategy into our approach could significantly save time and space in the model training ***: Our KGCodeSum approach could effectively generate code summaries.

关键词： Source code summarization encoder-decoder Information incorporation Dynamic vocabulary Deep learning

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Single Image Dehazing via Fusion of Multilevel Attention Network for Vision-Based Measurement Applications

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Sahu, Geet Seal, Ayan Jaworek-Korjakowska, Joanna Krejcar, Ondrej Pandit Dwarka Prasad Mishra Indian Inst Informat T Jabalpur 482005 India Amity Univ Amity Ctr Artificial Intelligence Noida 201301 India Univ Hradec Kralove Fac Informat & Management Ctr Basic & Appl Sci Hradec Kralove 50003 Czech Republic AGH Univ Sci & Technol Dept Automat Control & Robot PL-30059 Krakow Poland Univ Teknol Malaysia Malaysia Japan Int Inst Technol Kuala Lumpur 54100 Malaysia

Nowadays, researchers use vision-based measurement tools to record, detect, and monitor an atmospheric phenomenon called haze. It impedes the proper functioning of many outdoor industrial systems, such as autonomous driving, surveillance, satellite imagery, and so on. Conventional visibility restoration methods cannot accurately recover image quality due to inaccurate estimations of haze thickness and the presence of color-cast effects. Deep neural networks are evolving due to their ability to directly dehaze images from hazy scenes. Therefore, a unique attention-based end-to-end dehazing network named oval-net has been proposed in this study to restore clear images from its counterpart without employing the atmospheric scattering model. The oval-net is an encoder-decoder architecture that uses spatial and channel attention at each stage to focus on dominant and significant information while avoiding the transmission of irrelevant information from the encoder to the decoder, allowing quicker convergence. The proposed approach outperforms seven state-of-the-art algorithms in quantitative and qualitative assessments of a variety of synthetic and real-world hazy images, proving its effectiveness for vision-based industrial systems.

关键词： Atmospheric modeling Image restoration Image color analysis Atmospheric measurements Feature extraction Information filters Discrete wavelet transforms Channel attention (CA) encoder-decoder image dehazing spatial attention (SA) UNet

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Spatiotemporal Prediction of Ionospheric Total Electron Content Based on ED-ConvLSTM

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REMOTE SENSING 2023年第12期15卷 3064-3064页

作者： Li, Liangchao Liu, Haijun Le, Huijun Yuan, Jing Shan, Weifeng Han, Ying Yuan, Guoming Cui, Chunjie Wang, Junling Inst Intelligent Emergency Informat Proc Inst Disaster Prevent Langfang 065201 Peoples R China Chinese Acad Sci Inst Geol & Geophys Key Lab Earth & Planetary Phys Beijing 100029 Peoples R China Inst Disaster Prevent Sch Informat Engn Langfang 065201 Peoples R China Beijing Jingwei Text Machinery New Technol Co Ltd Beijing 100176 Peoples R China Nanjing Forestry Univ Coll Informat Sci & Technol Nanjing 210037 Peoples R China

Total electron content (TEC) is a vital parameter for describing the state of the ionosphere, and precise prediction of TEC is of great significance for improving the accuracy of the Global Navigation Satellite System (GNSS). At present, most deep learning prediction models just consider TEC temporal variation, while ignoring the impact of spatial location. In this paper, we propose a TEC prediction model, ED-ConvLSTM, which combines convolutional neural networks with recurrent neural networks to simultaneously consider spatiotemporal features. Our ED-ConvLSTM model is built based on the encoder-decoder architecture, which includes two modules: encoder module and decoder module. Each module is composed of ConvLSTM cells. The encoder module is used to extract the spatiotemporal features from TEC maps, while the decoder module converts spatiotemporal features into predicted TEC maps. We compared the predictive performance of our model with two traditional time series models: LSTM, GRU, a spatiotemporal mode1 ConvGRU, and the TEC daily forecast product C1PG provided by CODE on a total of 135 grid points in East Asia (10 & DEG;-45 & DEG;N, 90 & DEG;-130 & DEG;E). The experimental results show that the prediction error indicators MAE, RMSE, MAPE, and prediction similarity index SSIM of our model are superior to those of the comparison models in high, normal, and low solar activity years. The paper also analyzed the predictive performance of each model monthly. The experimental results indicate that the predictive performance of each model is influenced by the monthly mean of TEC. The ED-ConvLSTM model proposed in this paper is the least affected and the most stable by the monthly mean of TEC. Additionally, the paper compared the predictive performance of each model during two magnetic storm periods when TEC changes sharply. The results indicate that our ED-ConvLSTM model is least affected during magnetic storms and its predictive performance is superior to those of the c

关键词： ionospheric TEC ConvLSTM encoder-decoder spatiotemporal geomagnetic storm

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Translating medical image to radiological report: Adaptive multilevel multi-attention approach

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022年 221卷 106853-106853页

作者： Gajbhiye, Gaurav O. V. Nandedkar, Abhijeet Faye, Ibrahima SGGS Inst Engn & Technol CVPR Lab Nanded India Univ Teknol PETRONAS CISIR Seri Iskandar Malaysia

Acknowledgment This research work was supported by joint collaboration of Computer Vision and Pattern Vision (CVPR) Lab, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India and Center for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS (UTP), Seri Iskandar, Malaysia under International Grant 015ME0-018.

关键词： Radiology report generation X-ray encoder-decoder Residual attention module Multilevel multi-attention mechanism Radiology-trained word embedding

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A Fractal Curve-Inspired Framework for Enhanced Semantic Segmentation of Remote Sensing Images

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SENSORS 2024年第22期24卷 7159页

作者： Wang, Xinhua Yuan, Botao Li, Zhuang Wang, Heqi Northeast Elect Power Univ Sch Comp Sci Jilin 132012 Peoples R China Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China

The classification and recognition of features play a vital role in production and daily life;however, the current semantic segmentation of remote sensing images is hampered by background interference and other factors, leading to issues such as fuzzy boundary segmentation. To address these challenges, we propose a novel module for encoding and reconstructing multi-dimensional feature layers. Our approach first utilizes a bilinear interpolation method to downsample the multi-dimensional feature layer in the coding stage of the U-shaped framework. Subsequently, we incorporate a fractal curve module into the encoder, which aggregates points on feature maps from different layers, effectively grouping points from diverse regions. Finally, we introduce an aggregation layer that combines the upsampling method from the UNet series, employing the multi-scale censoring of multi-dimensional feature map outputs from various layers to efficiently capture both spatial and feature information. The experimental results across diverse scenarios demonstrate that our model achieves excellent performance in aggregating point information from feature maps, significantly enhancing semantic segmentation tasks.

关键词： remote sensing images bilinear interpolation fractal curve gather layers encoder-decoder semantic segmentation

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Label-aware Attention Network with Multi-scale Boosting for Medical Image Segmentation

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartD期255卷

作者： Wang, Linbo Xu, Peng Cao, Xianfeng Nappi, Michele Wan, Shaohua Anhui Univ Sch Comp Sci & Technol Key Lab Intelligent Comp & Signal Proc MOE Hefei 230601 Peoples R China Univ Salerno Dept Comp Sci Fisciano SA Italy Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen Peoples R China

Deep medical image segmentation calls for features with strong discrimination and rich scales due to ambiguous background distraction and large variations in object sizes and shapes. In this paper, we propose two modules to obtain these features. First, existing encoders tend to extract similar foreground/background features at blurry boundaries due to mixed-label feature aggregation. To enhance the discrimination of these features, a Label- Aware Attention (LAA) module is presented to reconstruct them by fusing same-label local features. The fusion is guided by local attention maps based on label-aware affinity learning. Second, instead of relying on a single encoder for scale context mining, we propose a Multi-scale Feature Boosting (MFB) module that applies parallel convolution with different receptive fields for scale embedding and integrates an additional backbone for cross- encoder scale reference. Combining LAA and MFB, a new encoder-decoder based framework is presented, where MFBs act as encoder blocks to recursively extract features with rich scale context, while LAA operates in the decoder layer to enhance the label-aware discriminativeness of features. Extensive experiments on three standard medical segmentation datasets demonstrate the effectiveness of the proposed framework.

关键词： Medical image segmentation Attention map encoder-decoder

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Dense Semantic Labeling with Atrous Spatial Pyramid Pooling and decoder for High-Resolution Remote Sensing Imagery

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REMOTE SENSING 2019年第1期11卷 20-20页

作者： Wang, Yuhao Liang, Binxiu Ding, Meng Li, Jiangyun Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Minist Educ Key Lab Knowledge Automat Ind Proc Beijing 100083 Peoples R China Thermo Fisher Sci Richardson TX 75081 USA

Dense semantic labeling is significant in high-resolution remote sensing imagery research and it has been widely used in land-use analysis and environment protection. With the recent success of fully convolutional networks (FCN), various types of network architectures have largely improved performance. Among them, atrous spatial pyramid pooling (ASPP) and encoder-decoder are two successful ones. The former structure is able to extract multi-scale contextual information and multiple effective field-of-view, while the latter structure can recover the spatial information to obtain sharper object boundaries. In this study, we propose a more efficient fully convolutional network by combining the advantages from both structures. Our model utilizes the deep residual network (ResNet) followed by ASPP as the encoder and combines two scales of high-level features with corresponding low-level features as the decoder at the upsampling stage. We further develop a multi-scale loss function to enhance the learning procedure. In the postprocessing, a novel superpixel-based dense conditional random field is employed to refine the predictions. We evaluate the proposed method on the Potsdam and Vaihingen datasets and the experimental results demonstrate that our method performs better than other machine learning or deep learning methods. Compared with the state-of-the-art DeepLab_v3+ our model gains 0.4% and 0.6% improvements in overall accuracy on these two datasets respectively.

关键词： remote sensing imagery dense semantic labeling fully convolutional networks atrous spatial pyramid pooling encoder-decoder superpixel-based DenseCRF

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Attention-shift based deep neural network for fine-grained visual categorization

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PATTERN RECOGNITION 2021年 116卷 107947-107947页

作者： Niu, Yi Jiao, Yang Shi, Guangming Xidian Univ Sch Elect Engn 2 Taibai South Rd Xian Shaanxi Peoples R China Xidian Univ Sch Artificial Intelligence 2 Taibai South Rd Xian Shaanxi Peoples R China

Fine-grained visual categorization (FGVC) has attracted extensive attention in recent years. The general pipeline of current FGVC techniques is to 1) locate the discriminative regions;2) extract features from each region independently;and 3) feed the integrated features to a classifier. In this paper, we reinvestigate the pipeline from the view of human visual recognition mechanisms. The perceiving of discriminative regions is a temporal processing by the human visual system (HVS) via the attention-shift mechanism. However, the existing independent feature extracting and one-pass feeding strategy ignore the inherent semantic relationships among discriminative regions, and thus is improper to model the attention-shift process properly. Therefore, in this paper, we propose a novel end-to-end FGVC network structure named Attention-Shift based Deep Neural Network (AS-DNN) to locate the discriminative regions automatically and encode the semantic correlations iteratively. AS-DNN consists of two channels: 1) the global perception channel C-glb and 2) the attention-shift channel C-sft, simulating the global perception and the attention-shift mechanism, respectively. Experimental results show that AS-DNN achieves state-of-the-art performances by outperforming both the CNN-based weakly or strongly-supervised FGVC algorithms on several widely-used fine-grained datasets, and the visualization of attention regions exhibit that the proposed method can locate the discriminative regions robustly in complex backgrounds and postures. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Fine-grained visual categorization Deep neural network Human perception mechanism Attention-shift encoder-decoder

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