Publications by authors named "Meiqing Pan"
Attention-based generative adversarial network in medical imaging: A narrative review.
Comput Biol Med
October 2022
Article Synopsis
- Generative Adversarial Networks (GANs) are models that help create and analyze images, and they're being used in medicine too!
- New attention mechanisms are like focusing on specific parts of an image, similar to how we see things, making GANs even better for understanding medical images.
- Researchers are starting to combine these attention techniques with GANs, showing promise for detecting health issues in medical images, but there's still a lot more to explore in this area.
Attention-Based Multi-Scale Generative Adversarial Network for synthesizing contrast-enhanced MRI.
Annu Int Conf IEEE Eng Med Biol Soc
November 2021
In clinical practice, about 35% of MRI scans are enhanced with Gadolinium - based contrast agents (GBCAs) worldwide currently. Injecting GBCAs can make the lesions much more visible on contrast-enhanced scans. However, the injection of GBCAs is high-risk, time-consuming, and expensive.
View Article and Find Full Text PDFPurpose: For timely treatment of extrahepatic metastasis and macrovascular invasion (aggressive progressive disease [PD]) in hepatocellular carcinoma, models aimed at stratifying the risks of subsequent aggressive PD should be constructed.
Patients And Methods: After dividing 332 patients from five hospitals into training (n = 236) and validation (n = 96) datasets, non-invasive models, including clinical/semantic factors (Model), deep learning radiomics (Model), and both (Model), were constructed to stratify patients according to the risk of aggressive PD. We examined the discrimination and calibration; similarly, we plotted a decision curve and devised a nomogram.
Background: This study evaluated the predictive value of gene signatures for biochemical recurrence (BCR) in primary prostate cancer (PCa) patients.
Methods: Clinical features and gene expression profiles of PCa patients were attained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets, which were further classified into a training set (n = 419), a validation set (n = 403). The least absolute shrinkage and selection operator Cox (LASSO-Cox) method was used to select discriminative gene signatures in training set for biochemical recurrence-free survival (BCRFS).