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Found 8 papers, 5 papers with code
Tell2Reg: Establishing spatial correspondence between images by the same language prompts
Spatial correspondence can be represented by pairs of segmented regions, such that the image registration networks aim to segment corresponding regions rather than predicting displacement fields or transformation parameters.
ZoRI: Towards Discriminative Zero-Shot Remote Sensing Instance Segmentation
Instance segmentation algorithms in remote sensing are typically based on conventional methods, limiting their application to seen scenarios and closed-set predictions.
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.
Competing for pixels: a self-play algorithm for weakly-supervised segmentation
Additionally, we propose a game termination condition that can be called by either side upon exhaustion of all ROI-containing patches, followed by the selection of a final patch from each.
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).
A Survey of Emerging Applications of Diffusion Probabilistic Models in MRI
Diffusion probabilistic models (DPMs) which employ explicit likelihood characterization and a gradual sampling process to synthesize data, have gained increasing research interest.
Rethinking Few-Shot Medical Segmentation: A Vector Quantization View
The existing few-shot medical segmentation networks share the same practice that the more prototypes, the better performance.
RTNet: Relation Transformer Network for Diabetic Retinopathy Multi-lesion Segmentation
Automatic diabetic retinopathy (DR) lesions segmentation makes great sense of assisting ophthalmologists in diagnosis.
