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1. Intensity-driven bounding box supervised brain white matter hyperintensities segmentation algorithm | |||
Cheng Ao | |||
Computer Science and Technology 08 March 2024 | |||
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Abstract:White matter hyperintensities (WMHs) serves as a crucial imaging feature for assessing cerebral white matter abnormalities, and accurate segmentation of WMHs holds significant importance for tracking disease progression, evaluating treatment effects, and studying and understanding various neurological and geriatric disorders. Presently, deep learning-based methods for WMHs segmentation rely heavily on extensively annotated training data at the pixel level. However, the irregular shapes, random distribution, and fuzzy boundaries characteristic of WMHs make acquiring pixel-level precise labels prohibitively costly. To mitigate the reliance on pixel-level annotations, this paper introduces an intensity-driven bounding box supervised brain white matter hyperintensities segmentation algorithm (IDBB), which substitutes precise labels with weak bounding box labels during model training. IDBB employs an intensity-based adaptive thresholding method to generate pixel-level pseudo-labels from bounding box labels and trains the segmentation network using both Dice loss and cross-entropy loss. Additionally, this paper introduces a WMHs segmentation dataset containing bounding box labels of various sizes, serving as a benchmark dataset for bounding box supervised WMHs segmentation tasks. Results demonstrate that the proposed method achieves segmentation performance on the Dice similarity coefficient (DSC) comparable to 90\% of fully supervised methods, surpassing other weakly supervised approaches. Experimental validation illustrates the effectiveness of the proposed method in reducing annotation costs while achieving satisfactory segmentation performance. | |||
TO cite this article:Cheng Ao. Intensity-driven bounding box supervised brain white matter hyperintensities segmentation algorithm[OL].[ 8 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4762582 |
2. Optimized LightGBM-Based Survival Prediction Model for ENKTL | |||
Wenke Lian,Yu Song,Dong Dong,Wenxiang Yang,Huafeng Zeng,Shibiao Xu,Li Guo,Fengyang An,Xuemei Zhu | |||
Computer Science and Technology 08 March 2024 | |||
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Abstract:With the rapid advancements in artificial intelligence and machine learning, particularly in fields like image recognition, time series forecasting, disease diagnostics, and certain tumor prognostics, these technologies have demonstrated clear advantages over traditional statistical methods, offering vital support for developing more precise predictive models. However, challenges arise due to frequent data omissions in clinical datasets and the intricate relationships between data and survival outcomes. This study specifically addresses the complex survival relationships in ENKTL by comparing discrete and continuous time survival prediction methodologies. It employs an HSIC-Lasso optimized LightGBM algorithm for discrete-time survival forecasting, successfully predicting ENKTL patient survival rates. By evaluating the impact of various interpolation techniques on the predictive accuracy of models dealing with missing values, this work enhances the precision of discrete-time survival forecasts. The findings not only offer fresh insights and strategies for navigating the complex survival dynamics in extranodal nasal NK/T lymphoma but also bolster technical support in this medical domain. This contributes to enhancing the accuracy of disease prognostics and equipping physicians with more targeted treatment options. | |||
TO cite this article:Wenke Lian,Yu Song,Dong Dong, et al. Optimized LightGBM-Based Survival Prediction Model for ENKTL[OL].[ 8 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4762512 |
3. DI-CFS:A Multi-Phase Feature Selection Method for Dimensionality Reduction | |||
Zhuo Liu,Chensheng Wang | |||
Computer Science and Technology 04 March 2024 | |||
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Abstract:Feature selection is critical in deep learning aiming to identify the most informative features in dimensionality reduction. In this paper, we propose a novel multi-phase feature selection method, namely Discrimination Improved Correlation-based Feature Selection (DI-CFS), which consists of three modules: the Discrimination Filtering Formula, the Isolation Forest (IF) algorithm, and the Correlation-based Feature Selection (CFS) method. In our method, the Discrimination Filtering Formula is employed to filter out invalid and insignificant features by calculating the discrimination value of them. The IF algorithm is utilized to remove redundant features which are more easily to be partitioned. The point-biserial correlation coefficient is utilized to calculate the weights of different features instead of the Pearson correlation coefficient, and the weights are evaluated by the Correlation-based Feature Selection (CFS) method. The experimental results show that the DI-CFS method is effective. | |||
TO cite this article:Zhuo Liu,Chensheng Wang. DI-CFS:A Multi-Phase Feature Selection Method for Dimensionality Reduction[OL].[ 4 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4762431 |
4. CPBNet:Concentrate,Parallel and Bimodal Network for Logistics Scene Text Detection and Recognition | |||
MA Yu-Chen | |||
Computer Science and Technology 28 February 2024 | |||
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Abstract:In the logistics industry, express sorting has always been an important link in ensuring smooth transportation of logistics. The recognition of logistics forms directly determines the sorting efficiency, but how to effectively improve the accuracy of text recognition of logistics forms in complex sorting environments is still a research challenge. In this article, we believe that the limitations of existing text models in logistics sorting scenarios mainly come from: 1) the interference caused by complex environments; 2) the incompatibility of recognition accuracy and speed; 3) single-modal limitation. Accordingly, this paper proposes CPBnet based on the principles of concentrate,parallel and bimodal. First, we corrected the form angularly, geometrically, and photometrically for complex scenes. Then, using a parallel method, the Attention mechanism is added to the visual model to guide the CTC training strategy, and the more accurate characteristics of the Attention model are used to train the backbone network to obtain better convolution features, and then the CTC branch is used for prediction, thereby ensuring Speed at inference. Finally, a language model is added after the visual model for semantic correction. The language model fully learns the input contextual information to make up for the visual semantic deficiency. There are basically very few pictures of sorting scenes in existing general text data sets. The lack of data in the field of sorting scenes has created a bottleneck for the application of deep learning in sorting scenes. Therefore, this article simulates real form data, prepares a sorting scene data set by itself, and proves through a large number of experiments that CPBNet has advantages on this data set and achieves the most advanced results. | |||
TO cite this article:MA Yu-Chen. CPBNet:Concentrate,Parallel and Bimodal Network for Logistics Scene Text Detection and Recognition[OL].[28 February 2024] http://en.paper.edu.cn/en_releasepaper/content/4762301 |
5. Relation Extraction Method for Chinese Public Opinion Based on Transferring Pre-trained Models and Merging Multiple Features | |||
ZHANG Yunkai,CHENG Bo | |||
Computer Science and Technology 27 February 2024 | |||
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Abstract:The public opinion has profoundly and extensively impacted the society, making the research on relation extraction(RE) in the field of public opinion crucial. Many existing relation extraction models either focus solely on basic information of Chinese characters or fail to fully leverage pre-trained models for extraction. Therefore, this paper proposes a relation extraction model, CwTransRE, which incorporates basic Chinese character information, glyph information, pinyin information and Chinese word information through transferring pre-trained models. CwTransRE enhances the effectiveness of relation extraction in two key aspects: firstly, besides basic Chinese character information, the integrated glyph, pinyin and Chinese word information enriches the semantic features of embeddings; secondly, the introduction of pre-trained models aids in obtaining more accurate embeddings, especially when dealing with relatively small training datasets. Experimental results on an open-source public opinion dataset demonstrate that our model achieves an F1 score of 0.703, outperforming NovelTagging, GraphRel(1p), GraphRel(2p) , TAG-JE and CasRel. | |||
TO cite this article:ZHANG Yunkai,CHENG Bo. Relation Extraction Method for Chinese Public Opinion Based on Transferring Pre-trained Models and Merging Multiple Features[OL].[27 February 2024] http://en.paper.edu.cn/en_releasepaper/content/4762184 |
6. Research on Option-Critic algorithm based Representation Erasure | |||
Meng JunWei,Hu Zheng | |||
Computer Science and Technology 06 February 2024 | |||
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Abstract:The Option-Critic (OC) framework can extract transferrable abstract knowledge without requiring any environment-specific prior knowledge, learning options (a form of temporal abstract policy) end-to-end. However, the OC framework exhibits lower data efficiency in transfer tasks. During the learning process, each option considers the entire task's state space, thereby increasing the scale of policy space search. This paper proposes an Option Learning algorithm based on Representation Erasure, which introduces the Representation Erasure method to clearly quantify the influence of each dimension on high-level and low-level policy learning. It identifies and erases dimensions that significantly interfere with training, effectively reducing the scale of policy space search. Through theoretical derivation and experimental validation, this paper demonstrates the effectiveness of the Representation Erasure-based Option Learning algorithm. | |||
TO cite this article:Meng JunWei,Hu Zheng. Research on Option-Critic algorithm based Representation Erasure[OL].[ 6 February 2024] http://en.paper.edu.cn/en_releasepaper/content/4762077 |
7. End-to-end 3D Human Pose Estimation using Dual Decoders | |||
WANG Zhang,SONG Mei,JIN Lei | |||
Computer Science and Technology 02 February 2024 | |||
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Abstract:Existing methods for 3D human pose estimation mainly divide the task into two stages. The first stage identifies the 2D coordinates of the human joints in the input image, namely the 2D human joint coordinates. The second stage uses the results from the first stage as input to recover the depth information of human joints from the 2D human joint coordinates to achieve 3D human pose estimation. However, the recognition accuracy of the two-stage method relies heavily on the results of the first stage and includes too many redundant processing steps, which reduces the inference efficiency of the network. To address these issues, we propose the EDD, a fully End-to-end 3D human pose estimation method based on transformer architecture with Dual Decoders. By learning multiple human poses, the model can directly infer all 3D human poses in the image using a pose decoder, and then further optimize the recognition result using a joint decoder based on the kinematic relations between joints. With the attention mechanism, this method can adaptively focus on the most relevant features to the target joint, effectively overcoming the feature misalignment problem in the human pose estimation task and greatly improving the model performance. Any complex post-processing step, such as non-maximum suppression, is eliminated, further improving the efficiency of the model. The results show that this method achieves an accuracy of 87.4\% on the MuPoTS-3D dataset, significantly improving the accuracy of the end-to-end 3D human pose estimation method based on mixed training. | |||
TO cite this article:WANG Zhang,SONG Mei,JIN Lei. End-to-end 3D Human Pose Estimation using Dual Decoders[OL].[ 2 February 2024] http://en.paper.edu.cn/en_releasepaper/content/4761949 |
8. Lightweight Deep Neural Network Model With Padding-free Downsampling | |||
LIU Dengfeng,GUO Xiaohe,WANG Ning,WU Qin | |||
Computer Science and Technology 25 January 2024 | |||
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Abstract:Deep neural networks have achieved impressive performance in image classification tasks. However, due to limitations in hardware resources, including computing units and storage capacity, deploying these networks directly on resource-constrained devices such as mobile and edge devices is challenging. While lightweight network models have made significant advancements, the downsampling stage has received little attention. As the feature map is reused multiple times, the reduction of its size during the downsampling stage not only reduces the computational cost of the downsampling module itself but also lowers the computational burden of subsequent stages. This paper addresses this gap by proposing a padding-free downsampling module that effectively reduces computational costs and can seamlessly integrates into various deep learning models. Furthermore, we introduce a hybrid stem layer to obtain competitive accuracy. Extensive experiments were conducted on CIFAR-100, Stanford Dogs, and ImageNet datasets. On the CIFAR-100 dataset, the results show that the proposed module reduces computational costs by approximately 20% and improves inference speed on resource-constrained devices by around 10%. | |||
TO cite this article:LIU Dengfeng,GUO Xiaohe,WANG Ning, et al. Lightweight Deep Neural Network Model With Padding-free Downsampling[OL].[25 January 2024] http://en.paper.edu.cn/en_releasepaper/content/4761964 |
9. Research on Deep Learning Stock Selection Method Based on Trend Decomposition Algorithm | |||
Wu Cheng-Hui,Zhang Hong-Jian | |||
Computer Science and Technology 20 June 2023 | |||
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Abstract:Some studies using machine learning and deep learning models did not fully explore other indicator features that affect stock price trends, resulting in significant prediction errors and difficulty in directly applying the prediction results to obtain excess returns. Therefore, it is necessary to establish an efficient deep learning model based on stock characteristics and fully explore the factors that affect stock price trends to improve prediction accuracy.In view of the shortcomings of traditional stock trend prediction, this paper constructs LSTM-BP neural network based on Tianniuxu optimization for stock price prediction, and constructs PCA-BP neural network model based on particle swarm optimization based on multi angle correlation analysis for trend prediction. Taking into account both price prediction and trend prediction dimensions, construct a deep quantitative stock selection method. In this process, a stock trend decomposition judgment algorithm is proposed to identify and classify the stock data, improve the data utilization, build a new data set, and use the particle swarm optimization to adjust and optimize the parameters.The results of strategy backtesting show that compared to other deep learning strategies and benchmark strategies, the stock selection method and strategy proposed in this article have significantly improved in terms of victory and return. This indicates that the model has good predictive ability and application potential, and can provide effective decision support for stock investment. | |||
TO cite this article:Wu Cheng-Hui,Zhang Hong-Jian. Research on Deep Learning Stock Selection Method Based on Trend Decomposition Algorithm[OL].[20 June 2023] http://en.paper.edu.cn/en_releasepaper/content/4760906 |
10. DrivCapsNet: A Driving Style Detection Algorithm Based on Capsule Network | |||
Yueyue Wang,Chenxing Wang,Haiyong Luo | |||
Computer Science and Technology 26 May 2023 | |||
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Abstract:Driver behavior plays a fundamental role in the driver-vehicle-environment system, where the driving style can significantly impact vehicle emissions, fuel consumption, insurance expenses, road safety, and advanced driver assistance systems (ADAS). Nonetheless, detecting driver behavior is a complex and challenging task, traditional methods require a lot of data pre-processing and there is still no research on discriminative driving behavior with capsule networks which can capture the spatial relationships of data. However, it has not been fully studied and applied for driver behavior detection. To tackle these challenges, we propose an methodology for detecting driving style using a capsule network, named DrivCapsNet, which is capable of detecting various driving styles using either inertial measurement unit (IMU) data or camera data. A crucial advantage of this method is that its dynamic routing mechanism can extract the relationships between the parts and the entity, thereby improving detection accuracy. We performed comprehensive experiments on two realistic driving datasets to substantiate the efficacy of our proposed DrivCapsNet approach. The outcomes validate that our approach performs well and achieves accurate driving style detection, highlighting its potential to contribute significantly to the field of driver behavior analysis. | |||
TO cite this article:Yueyue Wang,Chenxing Wang,Haiyong Luo. DrivCapsNet: A Driving Style Detection Algorithm Based on Capsule Network[OL].[26 May 2023] http://en.paper.edu.cn/en_releasepaper/content/4760841 |
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