Multiplanar analysis for pulmonary nodule classification in CT images using deep convolutional neural network and generative adversarial networks.
Yuya OnishiAtsushi TeramotoMasakazu TsujimotoTetsuya TsukamotoKuniaki SaitoHiroshi ToyamaKazuyoshi ImaizumiHiroshi FujitaPublished in: International journal of computer assisted radiology and surgery (2019)
This study reports development of a comprehensive analysis method to classify pulmonary nodules at multiple sections using GAN and DCNN. The effectiveness of the proposed discrimination method based on use of multiplanar images has been demonstrated to be improved compared to that realized in a previous study reported by the authors. In addition, the possibility of enhancing classification accuracy via application of GAN-generated images, instead of data augmentation, for pretraining even for medical datasets that contain relatively few images has also been demonstrated.
Keyphrases
- convolutional neural network
- deep learning
- machine learning
- optical coherence tomography
- artificial intelligence
- pulmonary hypertension
- healthcare
- computed tomography
- randomized controlled trial
- systematic review
- big data
- electronic health record
- magnetic resonance
- image quality
- contrast enhanced
- positron emission tomography
- light emitting