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Found 5 papers, 1 papers with code
Latent Diffusion Models with Image-Derived Annotations for Enhanced AI-Assisted Cancer Diagnosis in Histopathology
Finally, we show that synthetic data effectively trains AI models.
Investigating the use of publicly available natural videos to learn Dynamic MR image reconstruction
Purpose: To develop and assess a deep learning (DL) pipeline to learn dynamic MR image reconstruction from publicly available natural videos (Inter4K).
Deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields
Data used to train/test the model consisted of 3, 000 CFD simulations performed on synthetically generated 3D aortic shapes.
FReSCO: Flow Reconstruction and Segmentation for low latency Cardiac Output monitoring using deep artifact suppression and segmentation
Conclusion: FReSCO was successfully demonstrated for real-time monitoring of CO during exercise and could provide a convenient tool for assessment of the hemodynamic response to a range of stressors.
Machine Learning in Magnetic Resonance Imaging: Image Reconstruction
Following the success of machine learning in a wide range of imaging tasks, there has been a recent explosion in the use of machine learning in the field of MRI image reconstruction.
