Ms. Liyuan Zhang Profile

Liyuan Zhang

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Proceedings Article | 3 October 2024 Paper

A dual attention-guided transformer for multitissues segmentation of knee joint

Yixin Wang, Liyuan Zhang, Yu Miao, Xiongfeng Tang

Proceedings Volume 13272, 1327224 (2024) https://doi.org/10.1117/12.3048245

KEYWORDS: Transformers, Image segmentation, Tissues, Cartilage, Magnetic resonance imaging, Bone, Data modeling, Voxels, Feature extraction, Education and training

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Osteoarthritis is a leading cause of chronic disabilities. Automatic multi-tissues segmentation of knee joint can help doctors diagnose Osteoarthritis by segmenting knee joint MRI. However, manually segmenting is time-consuming for an experienced expert. Besides, this task is challenging due to the significant morphological differences and close proximity between various tissues in the knee joint. To achieve fast and accurate segmentation, we propose a novel hybrid architecture named nnCSCFormer (Not-another Cross-shaped Channel Transformer). This architecture integrates spatial and channel attention mechanisms to enable rapid and accurate automatic multi-tissues segmentation. By capturing the relationship between spatial and channel dimensions across the entire feature space, our model effectively extracts multitissues’specific information. Additionally, we introduce skip attention to aid the decoder in better preserving original image details. Experimental results demonstrate the efficacy of our model in simultaneously segmenting six tissues: femur bone, femoral cartilage, tibia bone, tibial cartilage, lateral meniscus and medial meniscus. The proposed method achieves superior segmentation performance compared to alternative methods on low-resolution knee MRI and has significant application value in preoperative planning for surgical navigation.

Proceedings Article | 21 April 2023 Paper

Improved YOLOv5 for breast mass detection based on attention mechanism

Fangfang Chen, Liyuan Zhang, Ke Zhang, Zhengang Jiang

Proceedings Volume 12602, 126021Q (2023) https://doi.org/10.1117/12.2668521

KEYWORDS: Feature fusion, Breast, Feature extraction, Cancer detection

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Because of the unclear boundaries and different shapes and sizes of breast masses, the accuracy of using traditional computer-aided diagnosis systems is low and it is difficult to meet the clinical requirements of physicians. In this paper, we propose a breast mass detection algorithm based on the combination of YOLOv5 and improved coordinate attention, to meet the clinical requirements of high accuracy and real-time. First, a novel backbone feature extraction network is constructed by combining the underlying backbone network and attention mechanism to fully learn useful features and suppress irrelevant features, thus enhancing the feature expression capability. Then a multi-path aggregation network is designed as the neck of feature fusion to fully fuse the feature information at different levels. Validation experiments are conducted on the DDSM breast mass dataset, and the results show that the network can accurately detect masses of different scales in different backgrounds with better real-time performance. Compared with the base YOLOv5, the network improves by 2.3% in accuracy.

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