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Found 6 papers, 3 papers with code
Full-dose Whole-body PET Synthesis from Low-dose PET Using High-efficiency Denoising Diffusion Probabilistic Model: PET Consistency Model
One of the most important tradeoffs in PET imaging is between image quality and radiation dose: high image quality comes with high radiation exposure.
Synthetic CT Generation from MRI using 3D Transformer-based Denoising Diffusion Model
The proposed model consists of two processes: a forward process which adds Gaussian noise to real CT scans, and a reverse process in which a shifted-window transformer V-net (Swin-Vnet) denoises the noisy CT scans conditioned on the MRI from the same patient to produce noise-free CT scans.
2D medical image synthesis using transformer-based denoising diffusion probabilistic model
In this paper, we introduce a medical image synthesis framework aimed at addressing the challenge of limited training datasets for AI models.
Cycle-guided Denoising Diffusion Probability Model for 3D Cross-modality MRI Synthesis
The two DDPMs exchange random latent noise in the reverse processes, which helps to regularize both DDPMs and generate matching images in two modalities.
RubyStar: A Non-Task-Oriented Mixture Model Dialog System
RubyStar is a dialog system designed to create "human-like" conversation by combining different response generation strategies.
Cost-Sensitive Reference Pair Encoding for Multi-Label Learning
Label space expansion for multi-label classification (MLC) is a methodology that encodes the original label vectors to higher dimensional codes before training and decodes the predicted codes back to the label vectors during testing.
