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Found 5 papers, 0 papers with code
Trustworthy Multi-phase Liver Tumor Segmentation via Evidence-based Uncertainty
Multi-phase liver contrast-enhanced computed tomography (CECT) images convey the complementary multi-phase information for liver tumor segmentation (LiTS), which are crucial to assist the diagnosis of liver cancer clinically.
When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation
Learning to segmentation without large-scale samples is an inherent capability of human.
Designing thermal radiation metamaterials via hybrid adversarial autoencoder and Bayesian optimization
Designing thermal radiation metamaterials is challenging especially for problems with high degrees of freedom and complex objective.
CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)
Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs.
Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising
However, the radiation dose reduction compromises the signal-to-noise ratio (SNR), leading to strong noise and artifacts that down-grade CT image quality.
