Point-of-Interest Preference Model Using an Attention Mechanism in a Convolutional Neural Network

Kasgari, Abbas Bagherian; Safavi, Sadaf; Nouri, Mohammadjavad; Hou, Jun; Sarshar, Nima; Ranjbarzadeh, Ramin

doi:10.3390/bioengineering10040495

Cited by 23 publications

(8 citation statements)

References 67 publications

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“…Instead, they enhance their focus on specific regions in specific sequence MRI scans. Attention mechanisms are considered a means for humans to quickly select the most important information from a large amount of data using limited processing resources [44]. This aligns well with the goal we aim to achieve.…”

Section: Plos Onementioning

confidence: 65%

MCE: Medical Cognition Embedded in 3D MRI feature extraction for advancing glioma staging

Xue,

Lu,

Wang

et al. 2024

PLoS ONE

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In recent years, various data-driven algorithms have been applied to the classification and staging of brain glioma MRI detection. However, the restricted availability of brain glioma MRI data in purely data-driven deep learning algorithms has presented challenges in extracting high-quality features and capturing their complex patterns. Moreover, the analysis methods designed for 2D data necessitate the selection of ideal tumor image slices, which does not align with practical clinical scenarios. Our research proposes an novel brain glioma staging model, Medical Cognition Embedded (MCE) model for 3D data. This model embeds knowledge characteristics into data-driven approaches to enhance the quality of feature extraction. Approach includes the following key components: (1) Deep feature extraction, drawing upon the imaging technical characteristics of different MRI sequences, has led to the design of two methods at both the algorithmic and strategic levels to mimic the learning process of real image interpretation by medical professionals during film reading; (2) We conduct an extensive Radiomics feature extraction, capturing relevant features such as texture, morphology, and grayscale distribution; (3) By referencing key points in radiological diagnosis, Radiomics feature experimental results, and the imaging characteristics of various MRI sequences, we manually create diagnostic features (Diag-Features). The efficacy of proposed methodology is rigorously evaluated on the publicly available BraTS2018 and BraTS2020 datasets. Comparing it to most well-known purely data-driven models, our method achieved higher accuracy, recall, and precision, reaching 96.14%, 93.4%, 97.06%, and 97.57%, 92.80%, 95.96%, respectively.

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Section: Plos Onementioning

confidence: 65%

MCE: Medical Cognition Embedded in 3D MRI feature extraction for advancing glioma staging

Xue,

Lu,

Wang

et al. 2024

PLoS ONE

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“…A recent study extended the utility of deep learning into the domain of Point-Of-Interest (POI) recommendation systems, proposing a model that incorporates an attention mechanism to better integrate user-centric features and contextual information. Through evaluating on established datasets such as Yelp and Gowalla, the model exhibited significant improvement in precision and recall for POI recommendations by attentively factoring in users’ geographical patterns [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

Optimizing Clinical Diabetes Diagnosis through Generative Adversarial Networks: Evaluation and Validation

García-Domínguez,

Galván-Tejada,

Magallanes-Quintanar

et al. 2023

Diseases

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The escalating prevalence of Type 2 Diabetes (T2D) represents a substantial burden on global healthcare systems, especially in regions such as Mexico. Existing diagnostic techniques, although effective, often require invasive procedures and labor-intensive efforts. The promise of artificial intelligence and data science for streamlining and enhancing T2D diagnosis is well-recognized; however, these advancements are frequently constrained by the limited availability of comprehensive patient datasets. To mitigate this challenge, the present study investigated the efficacy of Generative Adversarial Networks (GANs) for augmenting existing T2D patient data, with a focus on a Mexican cohort. The researchers utilized a dataset of 1019 Mexican nationals, divided into 499 non-diabetic controls and 520 diabetic cases. GANs were applied to create synthetic patient profiles, which were subsequently used to train a Random Forest (RF) classification model. The study’s findings revealed a notable improvement in the model’s diagnostic accuracy, validating the utility of GAN-based data augmentation in a clinical context. The results bear significant implications for enhancing the robustness and reliability of Machine Learning tools in T2D diagnosis and management, offering a pathway toward more timely and effective patient care.

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“…Our future work aims to improve the recognition accuracy of the DVI by using deep learning methods [ 49 , 50 ], validate the proposed DVI with more diverse subjects in various driving scenes, design an assistive controller to improve the overall driving performance and evaluate the impact of fatigue on DVI system performance, ideally by comparing it to real driving tasks during fatigue states. We will consider user satisfaction and subjective workload assessment to offer a more holistic evaluation.…”

Section: Discussionmentioning

confidence: 99%

A Novel Asynchronous Brain Signals-Based Driver–Vehicle Interface for Brain-Controlled Vehicles

Lian,

Guo,

Qiao

et al. 2023

Bioengineering

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Directly applying brain signals to operate a mobile manned platform, such as a vehicle, may help people with neuromuscular disorders regain their driving ability. In this paper, we developed a novel electroencephalogram (EEG) signal-based driver–vehicle interface (DVI) for the continuous and asynchronous control of brain-controlled vehicles. The proposed DVI consists of the user interface, the command decoding algorithm, and the control model. The user interface is designed to present the control commands and induce the corresponding brain patterns. The command decoding algorithm is developed to decode the control command. The control model is built to convert the decoded commands to control signals. Offline experimental results show that the developed DVI can generate a motion control command with an accuracy of 83.59% and a detection time of about 2 s, while it has a recognition accuracy of 90.06% in idle states. A real-time brain-controlled simulated vehicle based on the DVI was developed and tested on a U-turn road. Experimental results show the feasibility of the DVI for continuously and asynchronously controlling a vehicle. This work not only advances the research on brain-controlled vehicles but also provides valuable insights into driver–vehicle interfaces, multimodal interaction, and intelligent vehicles.

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Point-of-Interest Preference Model Using an Attention Mechanism in a Convolutional Neural Network

Cited by 23 publications

References 67 publications

MCE: Medical Cognition Embedded in 3D MRI feature extraction for advancing glioma staging

MCE: Medical Cognition Embedded in 3D MRI feature extraction for advancing glioma staging

Optimizing Clinical Diabetes Diagnosis through Generative Adversarial Networks: Evaluation and Validation

A Novel Asynchronous Brain Signals-Based Driver–Vehicle Interface for Brain-Controlled Vehicles

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