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Found 3 papers, 3 papers with code
T2-Only Prostate Cancer Prediction by Meta-Learning from Bi-Parametric MR Imaging
Current imaging-based prostate cancer diagnosis requires both MR T2-weighted (T2w) and diffusion-weighted imaging (DWI) sequences, with additional sequences for potentially greater accuracy improvement.
SAMReg: SAM-enabled Image Registration with ROI-based Correspondence
The distinct properties of the proposed ROI-based correspondence are discussed, in the context of potential benefits in clinical applications following image registration, compared with alternative correspondence-representing approaches, such as those based on sampled displacements and spatial transformation functions.
One registration is worth two segmentations
The goal of image registration is to establish spatial correspondence between two or more images, traditionally through dense displacement fields (DDFs) or parametric transformations (e. g., rigid, affine, and splines).
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