Semi-supervised Medical Image Segmentation
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Benchmarks
ACDC 10% labeled data
ACDC 20% labeled data
ACDC 5% labeled data
MM-WHS 2017
LA 5% labeled data
Most implemented
Stitching, Fine-tuning, Re-training: A SAM-enabled Framework for Semi-supervised 3D Medical Image Segmentation
Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation
ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast
Inherent Consistent Learning for Accurate Semi-supervised Medical Image Segmentation
Papers
VCDP: Variation-Conditioned Distributional Proxy Learning for Semi-Supervised Medical Image Segmentation
Semi-supervised 3D medical image segmentation reduces the need for dense voxel-level annotations by exploiting unlabeled volumes. Although existing methods such as consistency regularization, pseudo-labeling, and co-trai…
Semi-supervised Medical Image SegmentationSHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation
Recent advances in semi-supervised medical image segmentation have achieved remarkable performance through prediction consistency, pseudo-label supervision, and hard-region supervision. However, these methods primarily i…
Semi-supervised Medical Image SegmentationBeyond Random Sampling: Distribution-Aware Alignment for Semi-Supervised Medical Image Segmentation
Precise medical image segmentation is crucial for clinical diagnosis and treatment planning, yet relies heavily on expensive expert annotations. Semi-supervised medical image segmentation (SSMIS) offers a cost-effective …
Semi-supervised Medical Image SegmentationEmbracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision
Due to the scarcity of expert-annotated data, Semi-Supervised Medical Image Segmentation (SSMIS) has emerged as a promising approach. Many anatomical structures in medical images exhibit significant intra-class heterogen…
Semi-supervised Medical Image SegmentationContrastive LearningBeyond Visual Cues: CoT-Enhanced Reasoning for Semi-supervised Medical Image Segmentation
Semi-supervised medical image segmentation has emerged as a dominant research problem in medical image analysis, mitigating annotation scarcity by leveraging consistency regularization on unlabeled data. However, existin…
Semi-supervised Medical Image SegmentationQuality-Guided Semi-Supervised Learning for Medical Image Segmentation
Training accurate medical image segmentation models requires large amounts of densely annotated data, which is costly and time-consuming to obtain. Semi-supervised learning (SSL) alleviates this by learning from both abu…
Semi-supervised Medical Image Segmentation