Current evidence of generative artificial intelligence specifically developed for dental and maxillofacial radiology: a systematic review. [PDF]
Dentomaxillofac RadiolHung KF +5 more
europepmc +1 more source
Leveraging generative adversarial networks and SE-UNet for high-precision tobacco leaf image segmentation and blend uniformity detection. [PDF]
Sci RepDing M +7 more
europepmc +1 more source
Multi-site distributed training with data protections for PET-based synthetic CT. [PDF]
EJNMMI PhysJørgensen K +7 more
europepmc +1 more source
Lightweight CycleGAN models for cross-modality image transformation and experimental quality assessment in fluorescence microscopy. [PDF]
Biomed Opt ExpressSoltaninezhad M +9 more
europepmc +1 more source
Letter to the Editor on Generative AI-Enhanced Low-Dose CBCT for Impacted Mandibular Third Molars. [PDF]
Int Dent JXu C, Xu J.
europepmc +1 more source
A Lightweight Data-Augmented Deep Learning Framework for Real-Time Instance Segmentation in Liquid-Phase In Situ Transmission Electron Microscopy. [PDF]
ACS Meas Sci AuShen MH +5 more
europepmc +1 more source
Physics-embedded CycleGAN for robust metabolite mapping in short-readout deuterium metabolic imaging. [PDF]
Magn Reson LettHuang S +9 more
europepmc +1 more source
Cyclegan-VC2: Improved Cyclegan-Based Non-Parallel Voice Conversion [PDF]
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019 Non-parallel voice conversion (VC) is a technique for learning the mapping from source to target speech without relying on parallel data. This is an important task, but it has been challenging due to the disadvantages of the training conditions. Recently, CycleGAN-VC has provided a breakthrough and performed comparably to a parallel VC method without ...
Hirokazu Kameoka +2 more
exaly +4 more sources