Yiying Xie
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Yiying Xie has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Nian Cai
School of Information Engineering, Guangdong University of Technology, Guangzhou, China
1 shared publication - Ping Wang
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China
1 shared publication - Yinghong Zhou
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
1 shared publication - Yuchen Liang
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
1 shared publication - Zijie Cai
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
1 shared publication
Latest Publications
- Open Access
- 13 November 2024
- Published by MDPI AG in Electronics
- Vol. 13 (22) , 4443
Deep Learning Assisted Diagnosis of Chronic Obstructive Pulmonary Disease Based on a Local-to-Global Framework
To aid the diagnosis of chronic obstructive pulmonary disease (COPD), a local-to-global deep framework with group attentions and slice-aware loss is designed in this paper, which utilizes the chest CT sequences of the patients as the network input. To fully mine the medical hints submerged in the CT slices, two types of group attentions are designed to extract local–global features of the grouped slices. Specifically, in each group, a group local attention block (GLAB) and a group global attention block (GGAB) are designed to extract local features in the CT slices and long-range dependencies among the grouped slices. To alleviate the influence of different numbers of CT slices in the chest CT sequences for different patients, a slice-aware loss is proposed by incorporating a normalized coefficient into the cross-entropy loss. Experimental results indicate that the designed deep model performs a good COPD identification on a real COPD dataset with 96.08% accuracy, 94.12% sensitivity, 97.06% specificity, and 95.32% AUC, which is superior to some existing deep learning methods.
- 13 November 2024
- Published by MDPI AG in Electronics
- Vol. 13 (22) , 4443
Open AccessDeep Learning Assisted Diagnosis of Chronic Obstructive Pulmonary Disease Based on a Local-to-Global Framework
To aid the diagnosis of chronic obstructive pulmonary disease (COPD), a local-to-global deep framework with group attentions and slice-aware loss is designed in this paper, which utilizes the chest CT sequences of the patients as the network input. To fully mine the medical hints submerged in the CT slices, two types of group attentions are designed to extract local–global features of the grouped slices. Specifically, in each group, a group local attention block (GLAB) and a group global attention block (GGAB) are designed to extract local features in the CT slices and long-range dependencies among the grouped slices. To alleviate the influence of different numbers of CT slices in the chest CT sequences for different patients, a slice-aware loss is proposed by incorporating a normalized coefficient into the cross-entropy loss. Experimental results indicate that the designed deep model performs a good COPD identification on a real COPD dataset with 96.08% accuracy, 94.12% sensitivity, 97.06% specificity, and 95.32% AUC, which is superior to some existing deep learning methods.