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Found 5 papers, 3 papers with code
Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image Segmentation
To adapt SAM to medical imaging, existing methods primarily rely on tuning strategies that require extensive data or prior prompts tailored to the specific task, making it particularly challenging when only a limited number of data samples are available.
P-Mamba: Marrying Perona Malik Diffusion with Mamba for Efficient Pediatric Echocardiographic Left Ventricular Segmentation
In pediatric cardiology, the accurate and immediate assessment of cardiac function through echocardiography is important since it can determine whether urgent intervention is required in many emergencies.
MiTU-Net: A fine-tuned U-Net with SegFormer backbone for segmenting pubic symphysis-fetal head
The MiTU-Net model is based on an encoder-decoder framework, utilizing a pre-trained efficient transformer to enhance feature representation.
Lightweight Framework for Automated Kidney Stone Detection using coronal CT images
In this paper, we propose a lightweight fusion framework for kidney detection and kidney stone diagnosis on coronal CT images.
Evaluate Fine-tuning Strategies for Fetal Head Ultrasound Image Segmentation with U-Net
This method addresses the challenges associated with training a CNN network from scratch.
