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Found 9 papers, 3 papers with code
Pretraining Vision-Language Model for Difference Visual Question Answering in Longitudinal Chest X-rays
The model is developed using a step-by-step approach, starting with being pretrained on natural images and texts, followed by being trained using longitudinal chest X-ray data.
Generalizing Visual Question Answering from Synthetic to Human-Written Questions via a Chain of QA with a Large Language Model
Visual question answering (VQA) is a task where an image is given, and a series of questions are asked about the image.
Fast and accurate sparse-view CBCT reconstruction using meta-learned neural attenuation field and hash-encoding regularization
In the FACT method, we meta-trained a neural network and a hash-encoder using a few scans (= 15), and a new regularization technique is utilized to reconstruct the details of an anatomical structure.
Coil2Coil: Self-supervised MR image denoising using phased-array coil images
Hence, methods such as Noise2Noise (N2N) that require only pairs of noise-corrupted images have been developed to reduce the burden of obtaining training datasets.
Deep reinforcement learning-designed radiofrequency waveform in MRI
This work may lay the foundation for an emerging field of AI-driven RF waveform design.
DIFFnet: Diffusion parameter mapping network generalized for input diffusion gradient schemes and bvalues
In this study, a new deep neural network, referred to as DIFFnet, is developed to function as a generalized reconstruction tool of the diffusion-weighted signals for various gradient schemes and b-values.
DeepResp: Deep learning solution for respiration-induced B0 fluctuation artifacts in multi-slice GRE
DeepResp is designed to extract the respiration-induced phase errors from a complex image using deep neural networks.
Image and Video Processing
Deep Reinforcement Learning Designed Shinnar-Le Roux RF Pulse using Root-Flipping: DeepRF_SLR
A novel approach of applying deep reinforcement learning to an RF pulse design is introduced.
Artificial neural network for myelin water imaging
Purpose: To demonstrate the application of artificial-neural-network (ANN) for real-time processing of myelin water imaging (MWI).
Image and Video Processing
