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Found 4 papers, 4 papers with code
CMUNeXt: An Efficient Medical Image Segmentation Network based on Large Kernel and Skip Fusion
In order to extract global context information while taking advantage of the inductive bias, we propose CMUNeXt, an efficient fully convolutional lightweight medical image segmentation network, which enables fast and accurate auxiliary diagnosis in real scene scenarios.
Thinking Twice: Clinical-Inspired Thyroid Ultrasound Lesion Detection Based on Feature Feedback
Accurate detection of thyroid lesions is a critical aspect of computer-aided diagnosis.
Multi-Level Global Context Cross Consistency Model for Semi-Supervised Ultrasound Image Segmentation with Diffusion Model
Our approach enables the effective transfer of probability distribution knowledge to the segmentation network, resulting in improved segmentation accuracy.
CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network
However, due to the inherent local characteristics of ordinary convolution operations, U-Net encoder cannot effectively extract global context information.
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