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
- resistance training
- end stage renal disease
- healthcare
- chronic kidney disease
- pulmonary hypertension
- peritoneal dialysis
- prognostic factors
- magnetic resonance imaging
- machine learning
- high dose
- quality improvement
- cardiovascular events
- type diabetes
- magnetic resonance
- body composition
- patient reported outcomes
- label free
- sensitive detection
- quantum dots