Dynamic contrast-enhanced computed tomography diagnosis of primary liver cancers using transfer learning of pretrained convolutional neural networks: Is registration of multiphasic images necessary?
Akira YamadaKazuki OyamaSachie FujitaEriko YoshizawaFumihito IchinoheDaisuke KomatsuYasunari FujinagaPublished in: Int. J. Comput. Assist. Radiol. Surg. (2019)
Keyphrases
- computed tomography
- ct images
- transfer learning
- contrast enhanced
- ct scans
- rigid registration
- image registration
- medical imaging
- three dimensional
- medical images
- motion artifacts
- region of interest
- ct data
- registration accuracy
- mri data
- image data
- image reconstruction
- mr images
- convolutional neural networks
- input image
- magnetic resonance
- x ray
- lymph nodes
- image analysis
- magnetic resonance images
- clinical applications
- deformable registration
- computer aided diagnosis
- accurate registration
- cancer diagnosis
- nonrigid registration
- heart disease
- fully automatic
- deformation field
- prostate cancer
- machine learning
- text categorization
- segmentation algorithm