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Found 5 papers, 5 papers with code
MambaMorph: a Mamba-based Framework for Medical MR-CT Deformable Registration
Capturing voxel-wise spatial correspondence across distinct modalities is crucial for medical image analysis.
Ranked #1 on
Medical Image Registration
on SR-Reg
SAMIHS: Adaptation of Segment Anything Model for Intracranial Hemorrhage Segmentation
Segment Anything Model (SAM), a vision foundation model trained on large-scale annotations, has recently continued raising awareness within medical image segmentation.
Wide & deep learning for spatial & intensity adaptive image restoration
The wide & deep architecture in DparNet enables the learned parameters to directly modulate the final restoring results, boosting spatial & intensity adaptive image restoration.
Learning to "Segment Anything" in Thermal Infrared Images through Knowledge Distillation with a Large Scale Dataset SATIR
We discovered that this ability of SAM can be leveraged to pretrain models for specific fields.
Atmospheric Transmission and Thermal Inertia Induced Blind Road Segmentation with a Large-Scale Dataset TBRSD
The experimental results demonstrate that our method outperforms existing techniques and achieves state-of-the-art performance in thermal blind road segmentation, as validated on benchmark thermal infrared semantic segmentation datasets such as MFNet and SODA.
