Conditional generative adversarial network driven radiomic prediction of mutation status based on magnetic resonance imaging of breast cancer.
Zi Huai HuangLianghong ChenYan SunQian LiuPingzhao HuPublished in: Journal of translational medicine (2024)
Our study establishes cGAN as a viable tool for generating synthetic BC MRIs for mutation status prediction and subtype classification to better characterize the heterogeneity of BC in patients. The synthetic images also have the potential to significantly augment existing MRI data and circumvent issues surrounding data sharing and patient privacy for future BC machine learning studies.
Keyphrases
- machine learning
- magnetic resonance imaging
- big data
- deep learning
- end stage renal disease
- electronic health record
- contrast enhanced
- health information
- newly diagnosed
- artificial intelligence
- chronic kidney disease
- prognostic factors
- computed tomography
- peritoneal dialysis
- social media
- convolutional neural network
- single cell
- magnetic resonance
- patient reported outcomes
- climate change
- network analysis