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Found 5 papers, 5 papers with code
Shape-aware Segmentation of the Placenta in BOLD Fetal MRI Time Series
In this work, we propose a machine learning segmentation framework for placental BOLD MRI and apply it to segmenting each volume in a time series.
Dynamic Neural Fields for Learning Atlases of 4D Fetal MRI Time-series
We apply our method to learning subject-specific atlases and motion stabilization of dynamic BOLD MRI time-series of fetuses in utero.
Interpolating between Images with Diffusion Models
One little-explored frontier of image generation and editing is the task of interpolating between two input images, a feature missing from all currently deployed image generation pipelines.
Automatic Segmentation of the Placenta in BOLD MRI Time Series
In this work, we propose a machine learning model based on a U-Net neural network architecture to automatically segment the placenta in BOLD MRI and apply it to segmenting each volume in a time series.
Discretization Invariant Networks for Learning Maps between Neural Fields
With the emergence of powerful representations of continuous data in the form of neural fields, there is a need for discretization invariant learning: an approach for learning maps between functions on continuous domains without being sensitive to how the function is sampled.
