MHAU-Net: Skin Lesion Segmentation Based on Multi-Scale Hybrid Residual Attention Network

Li, Yingjie; Xu, Chao; Han, Jubao; An, Ziheng; Wang, Deyu; Ma, Haichao; Liu, Chuanxu

doi:10.3390/s22228701

Cited by 5 publications

(1 citation statement)

References 31 publications

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“…Anomaly detection is emphasized in areas such as data mining, computer vision, and deep learning. In recent years, the widespread adoption of deep learning has led to a series of deep anomaly detection methods, which have shown high practical performance in practical applications such as autonomous driving [6][7][8][9] and pathological detection [10,11]. According to the classification of supervision methods, anomaly detection methods based on deep learning can be divided into unsupervised, semi-supervised and weakly supervised methods.…”

Section: Introductionmentioning

confidence: 99%

Anomalous Behavior Detection with Spatiotemporal Interaction and Autoencoder Enhancement

Li,

Xie,

Zeng

et al. 2023

Electronics

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To reduce the cargo loss rate caused by abnormal consumption behavior in smart retail cabinets, two problems need to be solved. The first is that the diversity of consumers leads to a diversity of actions contained in the same behavior, which makes the accuracy of consumer behavior identification low. Second, the difference between normal interaction behavior and abnormal interaction behavior is small, and anomalous features are difficult to define. Therefore, we propose an anomalous behavior detection algorithm with human–object interaction graph convolution and confidence-guided difference enhancement. Aiming to solve the problem of low accuracy of consumer behavior recognition, including interactive behavior, the human–object interaction graph convolutional network is used to recognize action and extract video frames of abnormal human behavior. To define anomalies, we detect anomalies by delineating anomalous areas of the anomaly video frames. We use a confidence-guided anomaly enhancement module to perform confidence detection on the encoder-extracted coded features using a confidence full connection layer. The experimental results showed that the action recognition algorithm had good generalization ability and accuracy, and the screened video frames have obvious destruction characteristics, and the area under the receiver operating characteristic (AUROC) curve reached 82.8% in the detection of abnormal areas. Our research provides a new solution for the detection of abnormal behavior that destroys commodity packaging, which has considerable application value.

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Section: Introductionmentioning

confidence: 99%

Anomalous Behavior Detection with Spatiotemporal Interaction and Autoencoder Enhancement

Li,

Xie,

Zeng

et al. 2023

Electronics

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Automatic arrival-time picking of P- and S-waves of micro-seismic events based on relative standard generative adversarial network and GHRA

Cai,

Duan,

Yan

et al. 2024

J Petrol Explor Prod Technol

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Rapid, high-precision pickup of microseismic P- and S-waves is an important basis for microseismic monitoring and early warning. However, it is difficult to provide fast and highly accurate pickup of micro-seismic P- and S-waves arrival-time. To address this, the study proposes a lightweight and high-precision micro-seismic P- and S-waves arrival times picking model, lightweight adversarial U-shaped network (LAU-Net), based on the framework of the generative adversarial network, and successfully deployed in low-power devices. The pickup network constructs a lightweight feature extraction layer (GHRA) that focuses on extracting pertinent feature information, reducing model complexity and computation, and speeding up pickup. We propose a new adversarial learning strategy called application-aware loss function. By introducing the distribution difference between the predicted results and the artificial labels during the training process, we improve the training stability and further improve the pickup accuracy while ensuring the pickup speed. Finally, 8986 and 473 sets of micro-seismic events are used as training and testing sets to train and test the LAU-Net model, and compared with the STA/LTA algorithm, CNNDET+CGANet algorithm, and UNet++ algorithm, the speed of each pickup is faster than that of the other algorithms by 11.59ms, 15.19ms, and 7.79ms, respectively. The accuracy of the P-wave pickup is improved by 0.221, 0.01, and 0.029, respectively, and the S-wave pickup accuracy is improved by 0.233, 0.135, and 0.102, respectively. It is further applied in the actual project of the Shengli oilfield in Sichuan. The LAU-Net model can meet the needs of practical micro-seismic monitoring and early warning and provides a new way of thinking for accurate and fast on-time picking of micro-seismic P- and S-waves.

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A survey, review, and future trends of skin lesion segmentation and classification

Hasan

Ahamad

Yap

et al. 2023

Computers in Biology and Medicine

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MHAU-Net: Skin Lesion Segmentation Based on Multi-Scale Hybrid Residual Attention Network

Cited by 5 publications

References 31 publications

Anomalous Behavior Detection with Spatiotemporal Interaction and Autoencoder Enhancement

Anomalous Behavior Detection with Spatiotemporal Interaction and Autoencoder Enhancement

Automatic arrival-time picking of P- and S-waves of micro-seismic events based on relative standard generative adversarial network and GHRA

A survey, review, and future trends of skin lesion segmentation and classification

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