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- research-articleOctober 2024
Systematic comparison of deep-learning based fusion strategies for multi-modal ultrasound in diagnosis of liver cancer
- Ming-De Li,
- Wei Li,
- Man-Xia Lin,
- Xin-Xin Lin,
- Hang-Tong Hu,
- Ying-Chen Wang,
- Si-Min Ruan,
- Ze-Rong Huang,
- Rui-Fang Lu,
- Lv Li,
- Ming Kuang,
- Ming-De Lu,
- Li-Da Chen,
- Wei Wang,
- Qing-hua Huang
AbstractFor the diagnosis of liver cancer, conventional brightness mode (B-mode) can only provide morphological information. Multi-modal ultrasound, including shear-wave elastography (SWE) and contrast enhanced ultrasound (CEUS), can provide ...
- research-articleAugust 2024
Text Clustering and Natural Language Processing Based Dialogue System Model for Relieving Learning Weariness
CAICE '24: Proceedings of the 3rd International Conference on Computer, Artificial Intelligence and Control EngineeringPages 107–116https://doi.org/10.1145/3672758.3672776Learning weariness is a common psychological problem, which will affect students' learning effect and life quality. Currently, most ways to relieve the learning weariness require the intervention of a professional counselor or teacher, but these ...
- research-articleJanuary 2024
ADMNet: Adaptive-Weighting Dual Mapping for Online Tracking With Respiratory Motion Estimation in Contrast-Enhanced Ultrasound
- Ming-De Li,
- Hang-Tong Hu,
- Si-Min Ruan,
- Mei-Qing Cheng,
- Li-Da Chen,
- Ze-Rong Huang,
- Wei Li,
- Peng Lin,
- Hong Yang,
- Ming Kuang,
- Ming-De Lu,
- Qing-Hua Huang,
- Wei Wang
IEEE Transactions on Image Processing (TIP), Volume 33Pages 58–68https://doi.org/10.1109/TIP.2023.3333195Lesion localization and tracking are critical for accurate, automated medical imaging analysis. Contrast-enhanced ultrasound (CEUS) significantly enriches traditional B-mode ultrasound with contrast agents to provide high-resolution, real-time images of ...
- research-articleMarch 2023
SeisDeNet: an intelligent seismic data Denoising network for the internet of things
Journal of Cloud Computing: Advances, Systems and Applications (JOCCASA), Volume 12, Issue 1https://doi.org/10.1186/s13677-022-00378-3AbstractDeep learning (DL) has attracted tremendous interest in various fields in last few years. Convolutional neural networks (CNNs) based DL architectures have been successfully applied in computer vision, medical image processing, remote sensing, and ...
