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1,925 results on '"Jiang, Liping"'

10. Deep learning on tertiary lymphoid structures in hematoxylin-eosin predicts cancer prognosis and immunotherapy response

Author

Chen, Ziqiang, Wang, Xiaobing, Jin, Zelin, Li, Bosen, Jiang, Dongxian, Wang, Yanqiu, Jiang, Mengping, Zhang, Dandan, Yuan, Pei, Zhao, Yahui, Feng, Feiyue, Lin, Yicheng, Jiang, Liping, Wang, Chenxi, Meng, Weida, Ye, Wenjing, Wang, Jie, Qiu, Wenqing, Liu, Houbao, Huang, Dan, Hou, Yingyong, Wang, Xuefei, Jiao, Yuchen, Ying, Jianming, Liu, Zhihua, and Liu, Yun

Published
2024
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12. 3D EAGAN: 3D edge-aware attention generative adversarial network for prostate segmentation in transrectal ultrasound images

Author

Liu, Mengqing, Shao, Xiao, Jiang, Liping, and Wu, Kaizhi

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Automatic prostate segmentation in TRUS images has always been a challenging problem, since prostates in TRUS images have ambiguous boundaries and inhomogeneous intensity distribution. Although many prostate segmentation methods have been proposed, they still need to be improved due to the lack of sensibility to edge information. Consequently, the objective of this study is to devise a highly effective prostate segmentation method that overcomes these limitations and achieves accurate segmentation of prostates in TRUS images. A 3D edge-aware attention generative adversarial network (3D EAGAN)-based prostate segmentation method is proposed in this paper, which consists of an edge-aware segmentation network (EASNet) that performs the prostate segmentation and a discriminator network that distinguishes predicted prostates from real prostates. The proposed EASNet is composed of an encoder-decoder-based U-Net backbone network, a detail compensation module, four 3D spatial and channel attention modules, an edge enhance module, and a global feature extractor. The detail compensation module is proposed to compensate for the loss of detailed information caused by the down-sampling process of the encoder. The features of the detail compensation module are selectively enhanced by the 3D spatial and channel attention module. Furthermore, an edge enhance module is proposed to guide shallow layers in the EASNet to focus on contour and edge information in prostates. Finally, features from shallow layers and hierarchical features from the decoder module are fused through the global feature extractor to predict the segmentation prostates.

Published
2023

15. Clinical efficacy of laparoscopic peritoneal vaginoplasty in the treatment of MRKH syndrome

Author

Qin Chenglu, Shi Jinqiu, Jiang Liping, Wang Huizi, Liang Maolian, Ma Lili, Ren Junxu, Li Xueru, Zhang Longhui, and Li Meng

Subjects
mrkh syndrome, congenital absence of vagina, laparoscopy, peritoneal vaginoplasty, Medicine, Biotechnology, TP248.13-248.65
Abstract

Objective To investigate the clinical efficacy of laparoscopic peritoneal vaginoplasty (Luohu Type II) in the treatment of Mayer-Rokitansky-Küster-Hauser (MRKH) syndrome. Methods A retrospective analysis was conducted on the clinical data of 221 patients with MRKH syndrome treated at the Third Affiliated Hospital of Shenzhen University (Shenzhen Luohu People's Hospital) between January 2020 and April 2024.The patients were aged between 17 and 35 years, with an average age of (22±8) years. All patients underwent laparoscopic peritoneal vaginoplasty (Luohu Type II). Surgical time, hospital stay, intraoperative blood loss, and postoperative clinical outcomes were analyzed. Results All 221 patients in this group successfully underwent laparoscopic peritoneal vaginoplasty. The surgical time ranged from 25 to 100 minutes, with an average of (35±23) minutes. The hospital stay ranged from 15 to 29 days, with an average of (20±5) days. Intraoperative blood loss was between 10 and 100 mL. The preoperative vaginal indentation depth was 0.5 to 3 cm, and postoperative vaginal depth was (10±2) cm. The difference was statistically significant (P

Published
2024
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16. 3D genomic mapping reveals multifocality of human pancreatic precancers

Author

Braxton, Alicia M., Kiemen, Ashley L., Grahn, Mia P., Forjaz, André, Parksong, Jeeun, Mahesh Babu, Jaanvi, Lai, Jiaying, Zheng, Lily, Niknafs, Noushin, Jiang, Liping, Cheng, Haixia, Song, Qianqian, Reichel, Rebecca, Graham, Sarah, Damanakis, Alexander I., Fischer, Catherine G., Mou, Stephanie, Metz, Cameron, Granger, Julie, Liu, Xiao-Ding, Bachmann, Niklas, Zhu, Yutong, Liu, YunZhou, Almagro-Pérez, Cristina, Jiang, Ann Chenyu, Yoo, Jeonghyun, Kim, Bridgette, Du, Scott, Foster, Eli, Hsu, Jocelyn Y., Rivera, Paula Andreu, Chu, Linda C., Liu, Fengze, Fishman, Elliot K., Yuille, Alan, Roberts, Nicholas J., Thompson, Elizabeth D., Scharpf, Robert B., Cornish, Toby C., Jiao, Yuchen, Karchin, Rachel, Hruban, Ralph H., Wu, Pei-Hsun, Wirtz, Denis, and Wood, Laura D.

Published
2024
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17. Influencing Factors of Chinese EFL Students' Continuance Learning Intention in SPOC-Based Blended Learning Environment

Author

Jiang, Liping and Liang, Xiaodong

Abstract

The online and offline blended learning mode based on SPOC has gradually become a popular teaching mode in higher institutions due to COVID-19 pandemic. However, students of English as a Foreign Language (EFL) in SPOC-based blended learning environments continue facing problems of low participation and persistent intention. In order to explore the influencing factors of EFL students' continuance intentions to learn under the SPOC blended learning environment, this study recruits 48 EFL students from three higher vocational colleges based on the grounded theory. Through the triple coding method of open, axis, and selective coding based on the grounded theory and the used of software Nvivo to conduct a triple coding analysis of the text data obtained from the in-depth interviews and focus group interviews, a theoretical model of the influencing factors of EFL students' continuous learning intention (pre-influencing factors, external situational factors, and continuance intention) is proposed. Besides, a systematic framework based on stakeholders response to improve EFL students' continuance learning intentions in SPOC-based blended learning environments is constructed accordingly. This study can provide reference for basic theory and variable selection for subsequent research on the influencing factors of EFL students' continuance learning intentions in China and elsewhere.

Published
2023
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21. Teacher Feedback-Based Collaborative Testing Improves Students' Knowledge Gaps of Parasitology

Author

Peng, Xiaohong, Jiang, Liping, Cao, Deping, Chen, Gen, Li, Dayu, Teng, Pingying, and Li, Jianzhou

Abstract

Collaborative testing has been demonstrated the ability to improve students' performance, enhance students' learning, and aid in knowledge retention in many different courses. However, this examination mode lacks the process of teacher feedback. Herein, a short teacher feedback from was added immediately after the collaborative testing to improve the students' performance. A parasitology class of 121 undergraduates was randomized into two groups: "group A" and "group B." Collaborative testing was carried out at the end of theoretical teaching. During the test, students would first answer questions as individuals for 20 minutes. Then, students from "group A" answered the same questions in groups (5 students in each group) for 20 minutes, while the group-testing duration was only 15 minutes in "group B." Immediately after the group testing, teachers conducted a 5-minute feedback about the morphology identification according to the analysis of the answers by "group B." Four weeks later, a final test was conducted in an individual test. The total scores and scores for each examination content were analyzed. The results showed that there was no significant difference in the final exam scores between both groups (t = -1.278, P = 0.204). However, the morphological and diagnostic test results of the final examination in group B were significantly higher than those of the midterm examination, while there was no significant change in "group A" (t = 4.333, P = 0.051). The results confirmed that the teacher feedback after the collaborative testing can effectively make up for the students' knowledge gaps.

Published
2023
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22. Improving Hypernasality Estimation with Automatic Speech Recognition in Cleft Palate Speech

Author

Song, Kaitao, Wan, Teng, Wang, Bixia, Jiang, Huiqiang, Qiu, Luna, Xu, Jiahang, Jiang, Liping, Lou, Qun, Yang, Yuqing, Li, Dongsheng, Wang, Xudong, and Qiu, Lili

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Hypernasality is an abnormal resonance in human speech production, especially in patients with craniofacial anomalies such as cleft palate. In clinical application, hypernasality estimation is crucial in cleft palate diagnosis, as its results determine the subsequent surgery and additional speech therapy. Therefore, designing an automatic hypernasality assessment method will facilitate speech-language pathologists to make precise diagnoses. Existing methods for hypernasality estimation only conduct acoustic analysis based on low-resource cleft palate dataset, by using statistical or neural network-based features. In this paper, we propose a novel approach that uses automatic speech recognition model to improve hypernasality estimation. Specifically, we first pre-train an encoder-decoder framework in an automatic speech recognition (ASR) objective by using speech-to-text dataset, and then fine-tune ASR encoder on the cleft palate dataset for hypernasality estimation. Benefiting from such design, our model for hypernasality estimation can enjoy the advantages of ASR model: 1) compared with low-resource cleft palate dataset, the ASR task usually includes large-scale speech data in the general domain, which enables better model generalization; 2) the text annotations in ASR dataset guide model to extract better acoustic features. Experimental results on two cleft palate datasets demonstrate that our method achieves superior performance compared with previous approaches., Comment: Accepted by InterSpeech 2022

Published
2022

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