Lightweight Attention Convolutional Neural Network for Retinal Vessel Image Segmentation

Li, Xiang; Jiang, Yuchen; Li, Minglei; Yin, Shen

doi:10.1109/tii.2020.2993842

Cited by 168 publications

(32 citation statements)

References 35 publications

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“…The attention mechanism was proposed in 2014 and has achieved great success in machine translation [25]. In recent years, attention mechanisms have been widely used in various different types of deep learning tasks such as NLP [27] and image recognition [28] and have become one of the most interesting and insightful approaches in the DL field. In a representative study in 2017, the Google Brain team abandoned the classic RNN/CNN structure and proposed a transformer model composed of only the attention mechanism [29].…”

Section: Attention-based Modelmentioning

confidence: 99%

A Novel ABRM Model for Predicting Coal Moisture Content

Zhai

et al. 2022

J Intell Robot Syst

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Coal moisture content monitoring plays an important role in carbon reduction and clean energy decisions of coal transportation-storage aspects. Traditional coal moisture content detection mechanisms rely heavily on detection equipment, which can be expensive or difficult to deploy under field conditions. To achieve fast prediction of coal moisture content, a novel neural network model based on attention mechanism and bidirectional ResNet-LSTM structure (ABRM) is proposed in this paper. The prediction of coal moisture content is achieved by training the model to learn the relationship between changes of coal moisture content and meteorological conditions. The experimental results show that the proposed method has superior performance in terms of moisture content prediction accuracy compared with other state-of-the-art methods, and that ABRM model approaches appear to have the greatest potential for predicting coal moisture content shifts in the face of meteorological elements.

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Section: Attention-based Modelmentioning

confidence: 99%

A Novel ABRM Model for Predicting Coal Moisture Content

Zhai

et al. 2022

J Intell Robot Syst

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“…Deep learning methods are supervised, and image labeling is a difficult task [82]. Because the number of real medical images in training sets is very limited, it is impossible for deep learning models to learn all types of images from them.…”

Section: Annotations Sharingmentioning

confidence: 99%

When medical images meet generative adversarial network: recent development and research opportunities

Jiang

Rodríguez-Andina

et al. 2021

Discov Artif Intell

Self Cite

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Deep learning techniques have promoted the rise of artificial intelligence (AI) and performed well in computer vision. Medical image analysis is an important application of deep learning, which is expected to greatly reduce the workload of doctors, contributing to more sustainable health systems. However, most current AI methods for medical image analysis are based on supervised learning, which requires a lot of annotated data. The number of medical images available is usually small and the acquisition of medical image annotations is an expensive process. Generative adversarial network (GAN), an unsupervised method that has become very popular in recent years, can simulate the distribution of real data and reconstruct approximate real data. GAN opens some exciting new ways for medical image generation, expanding the number of medical images available for deep learning methods. Generated data can solve the problem of insufficient data or imbalanced data categories. Adversarial training is another contribution of GAN to medical imaging that has been applied to many tasks, such as classification, segmentation, or detection. This paper investigates the research status of GAN in medical images and analyzes several GAN methods commonly applied in this area. The study addresses GAN application for both medical image synthesis and adversarial learning for other medical image tasks. The open challenges and future research directions are also discussed.

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“…Neural networks are composed of neural units, which include network weights and biases that can be learned. Each neural unit calculates the inputs and outputs, namely forward propagation, according to some existing formulas of the neural network [29], as shown in the Equation (3). Where w and b are the weights to be trained, x is the input, and s is the output.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Research on Driver Status Recognition System of Intelligent Vehicle Terminal Based on Deep Learning

Xu¹,

Peng²,

Wang³

2021

WEVJ

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Automobile safety driving technology is a hot topic in today’s society, which is very significant to the social transportation system. Vehicle driving behavior monitoring is the foundation and core of safe driving techniques. The research on existing vehicle safety technology can not only improve the understanding of current safe driving research progress, but also provide reference for future researchers. This paper proposes a state recognition system based on a three-dimensional convolutional neural network, which can identify several improper states frequently encountered by drivers during driving, including drinking, making phone calls, and smoking, and can also issue alarm interventions. The system takes the collected continuous video frame information as the input of the three-dimensional convolutional network, carries out multi-level feature extraction and spatio-temporal information fusion, and identifies the driver state according to the extracted spatio-temporal features. The state is judged by the facial feature points of the video stream, and the design of the video surveillance driver state recognition system is completed. Then, the driver status recognition is improved and optimized, and finally, the actual deployment of the driver status recognition system on the mobile terminal is completed. A large number of experimental results show that the driver status recognition system proposed in this paper has achieved upper identification accuracy.

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Lightweight Attention Convolutional Neural Network for Retinal Vessel Image Segmentation

Cited by 168 publications

References 35 publications

A Novel ABRM Model for Predicting Coal Moisture Content

A Novel ABRM Model for Predicting Coal Moisture Content

When medical images meet generative adversarial network: recent development and research opportunities

Research on Driver Status Recognition System of Intelligent Vehicle Terminal Based on Deep Learning

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