Query：
学者姓名：李建强
Refining：
Year
Type
Indexed by
Source
Complex
Co-Author
Language
Clean All
Abstract ：
Pollen image classification is crucial for understanding allergic reactions and environmental impacts. In this study, we propose RESwinT, an enhanced deep learning model specifically designed for pollen image classification. RESwinT incorporates a parallel window transformer block with contextual information aggregation to expand the receptive field and facilitate information exchange between image patches. Additionally, a coordinate attention module is integrated to emphasize channel-specific features, improving the model's focus on salient pollen characteristics. Experiments conducted on a locally developed dataset of eight allergenic pollen types from Beijing, China, demonstrate that RESwinT achieves state-of-the-art performance, with an F1-score of 0.985 and an accuracy of 98.58%, surpassing existing CNN and transformer-based methods. These results highlight the effectiveness of RESwinT in pollen image classification and its potential for wider applications in environmental monitoring and healthcare.
Keyword ：
Parallel window transformer block Parallel window transformer block Pollen images classification Pollen images classification Coordinate attention Coordinate attention Swin transformer Swin transformer
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zu, Baokai , Cao, Tong , Li, Yafang et al. RESwinT: enhanced pollen image classification with parallel window transformer and coordinate attention [J]. | VISUAL COMPUTER , 2024 . |
| MLA | Zu, Baokai et al. "RESwinT: enhanced pollen image classification with parallel window transformer and coordinate attention" . | VISUAL COMPUTER (2024) . |
| APA | Zu, Baokai , Cao, Tong , Li, Yafang , Li, Jianqiang , Wang, Hongyuan , Wang, Quanzeng . RESwinT: enhanced pollen image classification with parallel window transformer and coordinate attention . | VISUAL COMPUTER , 2024 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
Disease diagnosis represents a critical and arduous endeavor within the medical field. Artificial intelligence (AI) techniques, spanning from machine learning and deep learning to large model paradigms, stand poised to significantly augment physicians in rendering more evidence-based decisions, thus presenting a pioneering solution for clinical practice. Traditionally, the amalgamation of diverse medical data modalities (e.g., image, text, speech, genetic data, physiological signals) is imperative to facilitate a comprehensive disease analysis, a topic of burgeoning interest among both researchers and clinicians in recent times. Hence, there exists a pressing need to synthesize the latest strides in multi-modal data and AI technologies in the realm of medical diagnosis. In this paper, we narrow our focus to five specific disorders (Alzheimer's disease, breast cancer, depression, heart disease, epilepsy), elucidating advanced endeavors in their diagnosis and treatment through the lens of artificial intelligence. Our survey not only delineates detailed diagnostic methodologies across varying modalities but also underscores commonly utilized public datasets, the intricacies of feature engineering, prevalent classification models, and envisaged challenges for future endeavors. In essence, our research endeavors to contribute to the advancement of diagnostic methodologies, furnishing invaluable insights for clinical decision making.
Keyword ：
disease diagnosis disease diagnosis artificial intelligence artificial intelligence multi-modal data multi-modal data machine learning machine learning deep learning deep learning large model large model
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Xu, Xi , Li, Jianqiang , Zhu, Zhichao et al. A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis [J]. | BIOENGINEERING-BASEL , 2024 , 11 (3) . |
| MLA | Xu, Xi et al. "A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis" . | BIOENGINEERING-BASEL 11 . 3 (2024) . |
| APA | Xu, Xi , Li, Jianqiang , Zhu, Zhichao , Zhao, Linna , Wang, Huina , Song, Changwei et al. A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis . | BIOENGINEERING-BASEL , 2024 , 11 (3) . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
Diabetic retinopathy (DR) significantly burdens ophthalmic healthcare due to its wide prevalence and high diagnostic costs. Especially in remote areas with limited medical access, undetected DR cases are on the rise. Our study introduces an advanced deep transfer learning-based system for real-time DR detection using fundus cameras to address this. This research aims to develop an efficient and timely assistance system for DR patients, empowering them to manage their health better. The proposed system leverages fundus imaging to collect retinal images, which are then transmitted to the processing unit for effective disease severity detection and classification. Comprehensive reports guide subsequent medical actions based on the identified stage. The proposed system achieves real-time DR detection by utilizing deep transfer learning algorithms, specifically VGGNet. The system's performance is rigorously evaluated, comparing its classification accuracy to previous research outcomes. The experimental results demonstrate the robustness of the proposed system, achieving an impressive 97.6% classification accuracy during the detection phase, surpassing the performance of existing approaches. Implementing the automated system in remote areas has transformed healthcare dynamics, enabling early, cost-effective DR diagnosis for millions. The system also streamlines patient prioritization, facilitating timely interventions for early-stage DR cases.
Keyword ：
Morphology Morphology Retinal fundus Retinal fundus Retinopathy Retinopathy Micro-aneurysms Micro-aneurysms Hemorrhage Hemorrhage Diabetes Diabetes Health issues Health issues
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Jabbar, Ayesha , Naseem, Shahid , Li, Jianqiang et al. Deep Transfer Learning-Based Automated Diabetic Retinopathy Detection Using Retinal Fundus Images in Remote Areas [J]. | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS , 2024 , 17 (1) . |
| MLA | Jabbar, Ayesha et al. "Deep Transfer Learning-Based Automated Diabetic Retinopathy Detection Using Retinal Fundus Images in Remote Areas" . | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 17 . 1 (2024) . |
| APA | Jabbar, Ayesha , Naseem, Shahid , Li, Jianqiang , Mahmood, Tariq , Jabbar, Kashif , Rehman, Amjad et al. Deep Transfer Learning-Based Automated Diabetic Retinopathy Detection Using Retinal Fundus Images in Remote Areas . | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS , 2024 , 17 (1) . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
Fundus images can be obtained non-invasively and be adopted to monitor the follow-up on various fundus diseases, such as high myopia. Therefore, the use of fundus images for the early screening of eye diseases has principal clinical significance. However, AI-based medical research continues to face two main challenges, lack of prior-knowledge guidance and complex fundus information. In this paper, we propose a pediatric high myopia classification model, the Attention-based Patch Residual Shrinkage network (APRSnet), which facilitates early clinical diagnosis by evaluating the importance of different image features. A collection of 2492 high-resolution fundus images of children, including 768 images of high myopia and 1724 images of non-high myopia, is fed to APRSnet as the training dataset. To better understand how different features impact the classification, APRSnet is tested on multiple feature-enhanced fusion datasets. The result shows that, by removing irrelevant information in fundus images, APRSnet achieves an accuracy of 0.959 and an F1 score of 0.946 and outperforms all classic image classification networks we compared with. We also testify that, in fundus images, gradient information benefits severity classification the most since it helps with model convergence more than luminance and texture information.
Keyword ：
Deep learning Deep learning Computer-aided diagnosis Computer-aided diagnosis Fundus images Fundus images Pediatric high myopia Pediatric high myopia
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Peng, Haoran , Li, Jianqiang , Cheng, Wenxiu et al. Automatic diagnosis of pediatric high myopia via Attention-based Patch Residual Shrinkage network [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 255 . |
| MLA | Peng, Haoran et al. "Automatic diagnosis of pediatric high myopia via Attention-based Patch Residual Shrinkage network" . | EXPERT SYSTEMS WITH APPLICATIONS 255 (2024) . |
| APA | Peng, Haoran , Li, Jianqiang , Cheng, Wenxiu , Zhao, Linna , Guan, Yu , Li, Zhaosheng et al. Automatic diagnosis of pediatric high myopia via Attention-based Patch Residual Shrinkage network . | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 255 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning.
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Iqbal, Saeed , Qureshi, Adnan N. , Li, Jianqiang et al. On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks [J]. | ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING , 2023 , 30 (5) : 3173-3233 . |
| MLA | Iqbal, Saeed et al. "On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks" . | ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING 30 . 5 (2023) : 3173-3233 . |
| APA | Iqbal, Saeed , Qureshi, Adnan N. , Li, Jianqiang , Mahmood, Tariq . On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks . | ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING , 2023 , 30 (5) , 3173-3233 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
Hyperspectral image (HSI) classification attempts to classify each pixel, which is an important means of obtaining land-cover knowledge. HSIs are cubic data with spectral-spatial knowledge and can generally be considered as sequential data alongside spectral dimension. Unlike convolutional neural networks (CNNs), which mainly focus on local relationship models in images, transformers have been shown to be a powerful structure for qualifying sequence data. However, it lacks the excellent ability of CNNs in establishing local relationships in images and cannot perform good generalization in the case of insufficient data. In addition, the gradient disappearance problem hinders the convergence stability of deep learning networks as the layers get deeper. To address these problems, we propose a Cascaded Convolution-based Transformer with Densely Connected Mechanism (CDCformer) for HSI classification. First, we propose a cascaded convolution feature tokenization to extract spectral-spatial information, which will introduce some inductive bias properties of CNN into the transformer. In addition, we design a simple and effective densely connected transformer to enhance feature propagation and transfer memorable information from shallow to deep layers. It efficiently improves the performance of the transformer and extracts more discriminative spectral-spatial features from the HSI. Extensive experimental evaluation of three public hyperspectral datasets shows that CDCformer achieves competitive classification results.
Keyword ：
Data mining Data mining convolutional neural networks (CNNs) convolutional neural networks (CNNs) Convolution Convolution transformer transformer Hyperspectral imaging Hyperspectral imaging Transformers Transformers Tokenization Tokenization Convolutional neural networks Convolutional neural networks hyperspectral image (HSI) classification hyperspectral image (HSI) classification Feature extraction Feature extraction Cascaded convolution feature tokenization (CCFT) Cascaded convolution feature tokenization (CCFT) densely connected mechanism (DCM) densely connected mechanism (DCM) deep learning deep learning
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zu, Baokai , Li, Yafang , Li, Jianqiang et al. Cascaded Convolution-Based Transformer With Densely Connected Mechanism for Spectral-Spatial Hyperspectral Image Classification [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2023 , 61 . |
| MLA | Zu, Baokai et al. "Cascaded Convolution-Based Transformer With Densely Connected Mechanism for Spectral-Spatial Hyperspectral Image Classification" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 61 (2023) . |
| APA | Zu, Baokai , Li, Yafang , Li, Jianqiang , He, Ziping , Wang, Hongyuan , Wu, Panpan . Cascaded Convolution-Based Transformer With Densely Connected Mechanism for Spectral-Spatial Hyperspectral Image Classification . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2023 , 61 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
This paper explores the concept of smart cities and the role of the Internet of Things (IoT) and machine learning (ML) in realizing a data-centric smart environment. Smart cities leverage technology and data to improve the quality of life for citizens and enhance the efficiency of urban services. IoT and machine learning have emerged as key technologies for enabling smart city solutions that rely on large-scale data collection, analysis, and decision-making. This paper presents an overview of smart cities' various applications and discusses the challenges associated with implementing IoT and machine learning in urban environments. The paper also compares different case studies of successful smart city implementations utilizing IoT and machine learning technologies. The findings suggest that these technologies have the potential to transform urban environments and enable the creation of more livable, sustainable, and efficient cities. However, significant challenges remain regarding data privacy, security, and ethical considerations, which must be addressed to realize the full potential of smart cities.
Keyword ：
Data acquisition technology Data acquisition technology Wireless and mobile networking Wireless and mobile networking Deep learning Deep learning Internet of Things (IoT) Internet of Things (IoT) Applications of smart cities Applications of smart cities Smart cities Smart cities
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Ullah, Amin , Anwar, Syed Myhammad , Li, Jianqiang et al. Smart cities: the role of Internet of Things and machine learning in realizing a data-centric smart environment [J]. | COMPLEX & INTELLIGENT SYSTEMS , 2023 , 10 (1) : 1607-1637 . |
| MLA | Ullah, Amin et al. "Smart cities: the role of Internet of Things and machine learning in realizing a data-centric smart environment" . | COMPLEX & INTELLIGENT SYSTEMS 10 . 1 (2023) : 1607-1637 . |
| APA | Ullah, Amin , Anwar, Syed Myhammad , Li, Jianqiang , Nadeem, Lubna , Mahmood, Tariq , Rehman, Amjad et al. Smart cities: the role of Internet of Things and machine learning in realizing a data-centric smart environment . | COMPLEX & INTELLIGENT SYSTEMS , 2023 , 10 (1) , 1607-1637 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
Accurate and fine-grained individual air quality index (IAQI) prediction is the basis of air quality index (AQI), which is of great significance for air quality control and human health. Traditional approaches, such as time series, recurrent neural network or graph convolutional network, cannot effectively integrate spatial-temporal and meteorological factors and manage the dynamic edge relationship among scattered monitoring stations. In this paper, a ST-CCN-IAQI model is proposed based on spatial-temporal causal convolution networks. Both the spatial effects of multi-source air pollutants and meteorological factors were considered via spatial attention mechanism. Time-dependent features in the causal convolution network were extracted by stacked dilated convolution and time attention. All the hyper-parameters in ST-CCN-IAQI were tuned by Bayesian optimization. Shanghai air monitoring station data were employed with a series of baselines (AR, MA, ARMA, ANN, SVR, GRU, LSTM and ST-GCN). Final results showed that: (1) For a single station, the RMSE and MAE values of ST-CCN-IAQI were 9.873 and 7.469, decreasing by 24.95% and 16.87% on average, respectively. R-2 was 0.917, with an average 5.69% improvement; (2) For all nine stations, the mean RMSE and MAE of ST-CCN-IAQI were 9.849 and 7.527, respectively, and the R-2 value was 0.906. (3) Shapley analysis showed PM10, humidity and NO2 were the most influencing factors in ST-CCN-IAQI. The Friedman test, under different resampling, further confirmed the advantage of ST-CCN-IAQI. The ST-CCN-IAQI provides a promising direction for fine-grained IAQI prediction.
Keyword ：
causal convolution network causal convolution network individual air quality index prediction individual air quality index prediction Shapley analysis Shapley analysis multi-source factors multi-source factors Bayesian optimization Bayesian optimization
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Liu, Xiliang , Zhao, Junjie , Lin, Shaofu et al. Fine-Grained Individual Air Quality Index (IAQI) Prediction Based on Spatial-Temporal Causal Convolution Network: A Case Study of Shanghai [J]. | ATMOSPHERE , 2022 , 13 (6) . |
| MLA | Liu, Xiliang et al. "Fine-Grained Individual Air Quality Index (IAQI) Prediction Based on Spatial-Temporal Causal Convolution Network: A Case Study of Shanghai" . | ATMOSPHERE 13 . 6 (2022) . |
| APA | Liu, Xiliang , Zhao, Junjie , Lin, Shaofu , Li, Jianqiang , Wang, Shaohua , Zhang, Yumin et al. Fine-Grained Individual Air Quality Index (IAQI) Prediction Based on Spatial-Temporal Causal Convolution Network: A Case Study of Shanghai . | ATMOSPHERE , 2022 , 13 (6) . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
Purpose: Computed tomography (CT) has the advantages of being low cost and noninvasive and is a primary diagnostic method for brain diseases. However, it is a challenge for junior radiologists to diagnose CT images accurately and comprehensively. It is necessary to build a system that can help doctors diagnose and provide an explanation of the predictions. Despite the success of deep learning algorithms in the field of medical image analysis, the task of brain disease classification still faces challenges: Researchers lack attention to complex manual labeling requirements and the incompleteness of prediction explanations. More importantly, most studies only measure the performance of the algorithm, but do not measure the effectiveness of the algorithm in the actual diagnosis of doctors. Methods: In this paper, we propose a model called DrCT2 that can detect brain diseases without using image-level labels and provide a more comprehensive explanation at both the slice and sequence levels. This model achieves reliable performance by imitating human expert reading habits: targeted scaling of primary images from the full slice scans and observation of suspicious lesions for diagnosis. We evaluated our model on two open-access data sets: CQ500 and the RSNA Intracranial Hemorrhage Detection Challenge. In addition, we defined three tasks to comprehensively evaluate model interpretability by measuring whether the algorithm can select key images with lesions. To verify the algorithm from the perspective of practical application, three junior radiologists were invited to participate in the experiments, comparing the effects before and after human-computer cooperation in different aspects. Results: The method achieved F1-scores of 0.9370 on CQ500 and 0.8700 on the RSNA data set. The results show that our model has good interpretability under the premise of good performance. Human radiologist evaluation experiments have proven that our model can effectively improve the accuracy of the diagnosis and improve efficiency. Conclusions: We proposed a model that can simultaneously detect multiple brain diseases. The report generated by the model can assist doctors in avoiding missed diagnoses, and it has good clinical application value.
Keyword ：
human-Al interaction human-Al interaction interpretability interpretability attention mechanism attention mechanism medical image classification medical image classification
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Wang, Ruiqian , Fu, Guanghui , Li, Jianqiang et al. Diagnosis after zooming in: A multilabel classification model by imitating doctor reading habits to diagnose brain diseases [J]. | MEDICAL PHYSICS , 2022 , 49 (11) : 7054-7070 . |
| MLA | Wang, Ruiqian et al. "Diagnosis after zooming in: A multilabel classification model by imitating doctor reading habits to diagnose brain diseases" . | MEDICAL PHYSICS 49 . 11 (2022) : 7054-7070 . |
| APA | Wang, Ruiqian , Fu, Guanghui , Li, Jianqiang , Pei, Yan . Diagnosis after zooming in: A multilabel classification model by imitating doctor reading habits to diagnose brain diseases . | MEDICAL PHYSICS , 2022 , 49 (11) , 7054-7070 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
The effective classification of pollen is a critical method to the prevention of pollen allergy. The conventional pollen classification primarily relies on manual handling under the microscope, which does not only require a lot of manpower but also has low classicization accuracy on results. In this paper, the optical microscope was used to scan the slides and the pollen image dataset was made for classification. We found from the pollen dataset that most of the images have clear contours, but there were also many pollen images with the following problems. One is that the pollen was covered by impurities like dust and pebbles, and the other is that the pollen images stain unevenly and the pollen image was blurred and the number of pollens was unbalanced. This paper proposes a Dual-Channel Attention method, we call it DCANet, which combines the advantages of channel attention and channel self-attention to improve the classification accuracy. The results of classification activation map analysis showed that DCANet paid more attention to pollen images.
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Li, Yanan , Li, Jianqiang , Pei, Yan et al. A Dual-channel Attention Model for Optical Microscope Pollen Classification [J]. | 2022 8TH INTERNATIONAL CONFERENCE ON CONTROL, DECISION AND INFORMATION TECHNOLOGIES (CODIT'22) , 2022 : 1118-1123 . |
| MLA | Li, Yanan et al. "A Dual-channel Attention Model for Optical Microscope Pollen Classification" . | 2022 8TH INTERNATIONAL CONFERENCE ON CONTROL, DECISION AND INFORMATION TECHNOLOGIES (CODIT'22) (2022) : 1118-1123 . |
| APA | Li, Yanan , Li, Jianqiang , Pei, Yan , Wang, Jin . A Dual-channel Attention Model for Optical Microscope Pollen Classification . | 2022 8TH INTERNATIONAL CONFERENCE ON CONTROL, DECISION AND INFORMATION TECHNOLOGIES (CODIT'22) , 2022 , 1118-1123 . |
| Export to | NoteExpress RIS BibTex |
Export
| Results： |
Selected to |
| Format： |
