Pulmonary nodule detection in low dose computed tomography using a medical-to-medical transfer learning approach.
Jenita ManokaranRicha MittalUkwatta ErangaPublished in: Journal of medical imaging (Bellingham, Wash.) (2024)
In this study, a semi-automated method was developed to detect lung nodules in LDCTs using HDCT pre-trained weights as the initial weights and retraining the model. Further, the results were compared by replacing HDCT pre-trained weights in the above approach with COCO pre-trained weights. The proposed method may identify early lung nodules during the screening program, reduce overdiagnosis and follow-ups due to misdiagnosis in LDCTs, start treatment options in the affected patients, and lower the mortality rate.
Keyphrases
- low dose
- computed tomography
- end stage renal disease
- resistance training
- healthcare
- ejection fraction
- chronic kidney disease
- newly diagnosed
- pulmonary hypertension
- magnetic resonance imaging
- peritoneal dialysis
- high dose
- prognostic factors
- positron emission tomography
- cardiovascular events
- body composition
- high throughput
- magnetic resonance
- coronary artery disease
- loop mediated isothermal amplification
- label free
- high intensity
- image quality