Xiangde Luo
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Explore the profile of Xiangde Luo including associated specialties, affiliations and a list of published articles.
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18
Citations
94
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Recent Articles
1.
Liao W, Luo X, Li L, Xu J, He Y, Huang H, et al.
Sci Rep
. 2025 Feb;
15(1):4250.
PMID: 39905029
To develop a deep learning model using transfer learning for automatic detection and segmentation of neck lymph nodes (LNs) in computed tomography (CT) images, the study included 11,013 annotated LNs...
2.
Luo X, Fu J, Zhong Y, Liu S, Han B, Astaraki M, et al.
Med Image Anal
. 2025 Jan;
101:103447.
PMID: 39756265
Radiation therapy is a primary and effective treatment strategy for NasoPharyngeal Carcinoma (NPC). The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly...
3.
Luo X, Wang H, Xu J, Li L, Zhao Y, He Y, et al.
Int J Radiat Oncol Biol Phys
. 2024 Nov;
121(5):1384-1393.
PMID: 39557309
Purpose: To develop a deep learning method exploiting active learning and source-free domain adaptation for gross tumor volume delineation in nasopharyngeal carcinoma (NPC), addressing the variability and inaccuracy when deploying...
4.
Luo X, Liao W, Zhao Y, Qiu Y, Xu J, He Y, et al.
Sci Data
. 2024 Oct;
11(1):1085.
PMID: 39366975
The deep learning (DL)-based prediction of accurate lymph node (LN) clinical target volumes (CTVs) for nasopharyngeal carcinoma (NPC) radiotherapy (RT) remains challenging. One of the main reasons is the variability...
5.
Chen J, Mei J, Li X, Lu Y, Yu Q, Wei Q, et al.
Med Image Anal
. 2024 Aug;
97:103280.
PMID: 39096845
Medical image segmentation is crucial for healthcare, yet convolution-based methods like U-Net face limitations in modeling long-range dependencies. To address this, Transformers designed for sequence-to-sequence predictions have been integrated into...
6.
Han M, Luo X, Xie X, Liao W, Zhang S, Song T, et al.
Med Image Anal
. 2024 Jul;
97:103274.
PMID: 39043109
High performance of deep learning on medical image segmentation rely on large-scale pixel-level dense annotations, which poses a substantial burden on medical experts due to the laborious and time-consuming annotation...
7.
Wang H, Chen J, Zhang S, He Y, Xu J, Wu M, et al.
IEEE Trans Med Imaging
. 2024 Jun;
43(12):4078-4090.
PMID: 38861437
Nasopharyngeal carcinoma (NPC) is a prevalent and clinically significant malignancy that predominantly impacts the head and neck area. Precise delineation of the Gross Tumor Volume (GTV) plays a pivotal role...
8.
Xu Z, Wang Y, Lu D, Luo X, Yan J, Zheng Y, et al.
Med Image Anal
. 2023 Jul;
88:102880.
PMID: 37413792
Semi-supervised learning has greatly advanced medical image segmentation since it effectively alleviates the need of acquiring abundant annotations from experts, wherein the mean-teacher model, known as a milestone of perturbed...
9.
Liao W, Luo X, He Y, Dong Y, Li C, Li K, et al.
Int J Radiat Oncol Biol Phys
. 2023 May;
117(4):994-1006.
PMID: 37244625
Purpose: Our purpose was to develop a deep learning model (AbsegNet) that produces accurate contours of 16 organs at risk (OARs) for abdominal malignancies as an essential part of fully...
10.
Li Z, Li C, Luo X, Zhou Y, Zhu J, Xu C, et al.
IEEE Trans Med Imaging
. 2023 Apr;
42(9):2666-2677.
PMID: 37030826
Recognition and quantitative analytics of histopathological cells are the golden standard for diagnosing multiple cancers. Despite recent advances in deep learning techniques that have been widely investigated for the automated...