medical image segmentation
Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation
V olume-wise labeling in 3D medical images is a time-consuming task that requires expertise. As a result, there is growing interest in using semi-supervised learning (SSL) techniques to train models with limited labeled data.
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SegV ol: Universal and Interactive Volumetric Medical Image Segmentation
Precise image segmentation provides clinical study with instructive information.
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Keypoint-Augmented Self-Supervised Learning for Medical Image Segmentation with Limited Annotation
Pretraining CNN models (i.e., UNet) through self-supervision has become a powerful approach to facilitate medical image segmentation under low annotation regimes.
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Class-AwareAdversarialTransformers forMedicalImageSegmentation
We followγ = 2in the original paper. As we can see, the setting using Focal loss and the other (i.e., Dice+Cross-Entropy)achievesimilarperformances.
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