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Ying Kou on Muck Rack

Ying Kou

As seen in: Wiley Online Library, Frontiers, EJNMMI Research, Optica
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Articles

Differentiating lymphoma progression from benign lesions: features of incident [ 18 F]FDG-avid foci on interim and end-of-treatment PET/CT

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By Jingshi Yang, Zhuzhong Cheng, Ying Kou
| Frontiers
Abstract Objective: This study aimed to identify distinguishing characteristics of new lesions on interim or end-of-treatment [18F]FDG PET/CT scans to differentiate lymphoma-related from benign lesions. Methods: We retrospectively analyzed 20 patients who developed new lesions during chemotherapy and performed [18F]FDG PET/CT scans in our department from January 2019 to December 2020.
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Clinical significance of diffuse [18F] AlF-NOTA-FAPI-04 uptake in the thyroid on PET/CT imaging - EJNMMI Research

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By Ying Kou, Wei Diao, Zhuzhong Cheng, Yutang Yao
| EJNMMI Research Verified
The retrospective study was approved by the ethics committee of Sichuan Cancer Hospital (SCCHEC-04–2020-001) and registered at the Chinese Clinical Trial Registry (ChiCTR2200061832). Written informed consent was obtained from each participant prior to 18F-FAPI PET/CT imaging. A total of 127 patients with 135 FAPI PET scans diagnosed or suspected of cancer underwent 18F-FAPI PET/CT for tumor diagnosis or staging in our department from Jan 2021 to Oct 2023.
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ASER: Adapted squared error relevance for rare cases prediction in imbalanced regression

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By Ying Kou, Guang-Hui Fu
| Wiley Online Library Verified
REFERENCES 1, , , , . Class-balanced loss based on effective number of samples. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2019: 9268-9277. 2. Learning from imbalanced data: open challenges and future directions. Prog Artif Intell. 2016; 5(4): 221-232. 3, , . Classification of imbalanced data: a review. Int J Pattern Recognit Artif Intell. 2009; 23(4): 687-719. 4, , , . Diagnosis of type 2 diabetes using cost-sensitive learning.
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