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Found 5 papers, 0 papers with code
Report on the AAPM Grand Challenge on deep generative modeling for learning medical image statistics
The goal of this challenge was to promote the development of deep generative models (DGMs) for medical imaging and to emphasize the need for their domain-relevant assessment via the analysis of relevant image statistics.
Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context
However, there remains an important need to understand the extent to which DDPMs can reliably learn medical imaging domain-relevant information, which is referred to as `spatial context' in this work.
AmbientFlow: Invertible generative models from incomplete, noisy measurements
In applications such as computed imaging, it is often difficult to acquire such data due to requirements such as long acquisition time or high radiation dose, while acquiring noisy or partially observed measurements of these objects is more feasible.
Investigating the robustness of a learning-based method for quantitative phase retrieval from propagation-based x-ray phase contrast measurements under laboratory conditions
In this work, a LBM was assessed for its applicability under practical scenarios by evaluating its robustness and generalizability under typical experimental variations.
A Method for Evaluating Deep Generative Models of Images via Assessing the Reproduction of High-order Spatial Context
We designed several stochastic context models (SCMs) of distinct image features that can be recovered after generation by a trained GAN.
