Papers Semi-supervised Medical Image Segmentation
“Semi-supervised Medical Image Segmentation” 태그가 달린 논문 177편 · 필터 해제
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 SegmentationSemiGDA: Generative Dual-distribution Alignment for Semi-Supervised Medical Image Segmentation
Semi-supervised learning addresses label scarcity and high annotation costs in medical image segmentation by exploiting the latent information in unlabeled data to enhance model performance. Traditional discriminative se…
Semi-supervised Medical Image SegmentationRepresentation LearningBCMDA: Bidirectional Correlation Maps Domain Adaptation for Mixed Domain Semi-Supervised Medical Image Segmentation
In mixed domain semi-supervised medical image segmentation (MiDSS), achieving superior performance under domain shift and limited annotations is challenging. This scenario presents two primary issues: (1) distributional …
Semi-supervised Medical Image SegmentationDomain AdaptationA Semi-Supervised Framework for Breast Ultrasound Segmentation with Training-Free Pseudo-Label Generation and Label Refinement
Semi-supervised learning (SSL) has emerged as a promising paradigm for breast ultrasound (BUS) image segmentation, but it often suffers from unstable pseudo labels under extremely limited annotations, leading to inaccura…
Semi-supervised Medical Image SegmentationContrastive LearningSemantic Class Distribution Learning for Debiasing Semi-Supervised Medical Image Segmentation
Medical image segmentation is critical for computer-aided diagnosis. However, dense pixel-level annotation is time-consuming and costly, and medical datasets often exhibit severe class imbalance. Such an imbalance causes…
Semi-supervised Medical Image SegmentationSRA-Seg: Synthetic to Real Alignment for Semi-Supervised Medical Image Segmentation
Synthetic data, an appealing alternative to extensive expert-annotated data for medical image segmentation, consistently fails to improve segmentation performance despite its visual realism. The reason being that synthet…
Semi-supervised Medical Image SegmentationUCAD: Uncertainty-guided Contour-aware Displacement for semi-supervised medical image segmentation
Existing displacement strategies in semi-supervised segmentation only operate on rectangular regions, ignoring anatomical structures and resulting in boundary distortions and semantic inconsistency. To address these issu…
Semi-supervised Medical Image SegmentationDomain-invariant Mixed-domain Semi-supervised Medical Image Segmentation with Clustered Maximum Mean Discrepancy Alignment
Deep learning has shown remarkable progress in medical image semantic segmentation, yet its success heavily depends on large-scale expert annotations and consistent data distributions. In practice, annotations are scarce…
Semi-supervised Medical Image SegmentationSemantic SegmentationDomain AdaptationVQ-Seg: Vector-Quantized Token Perturbation for Semi-Supervised Medical Image Segmentation
Consistency learning with feature perturbation is a widely used strategy in semi-supervised medical image segmentation. However, many existing perturbation methods rely on dropout, and thus require a careful manual tunin…
Semi-supervised Medical Image SegmentationImage ReconstructionBidirectional Channel-selective Semantic Interaction for Semi-Supervised Medical Segmentation
Semi-supervised medical image segmentation is an effective method for addressing scenarios with limited labeled data. Existing methods mainly rely on frameworks such as mean teacher and dual-stream consistency learning. …
Semi-supervised Medical Image SegmentationHarmonizing Generalization and Specialization: Uncertainty-Informed Collaborative Learning for Semi-supervised Medical Image Segmentation
Vision foundation models have demonstrated strong generalization in medical image segmentation by leveraging large-scale, heterogeneous pretraining. However, they often struggle to generalize to specialized clinical task…
Semi-supervised Medical Image SegmentationGeneral KnowledgeSSL-MedSAM2: A Semi-supervised Medical Image Segmentation Framework Powered by Few-shot Learning of SAM2
Despite the success of deep learning based models in medical image segmentation, most state-of-the-art (SOTA) methods perform fully-supervised learning, which commonly rely on large scale annotated training datasets. How…
Semi-supervised Medical Image SegmentationLiver SegmentationFew-Shot LearningVision-Language Enhanced Foundation Model for Semi-supervised Medical Image Segmentation
Semi-supervised learning (SSL) has emerged as an effective paradigm for medical image segmentation, reducing the reliance on extensive expert annotations. Meanwhile, vision-language models (VLMs) have demonstrated strong…
Semi-supervised Medical Image SegmentationDualFete: Revisiting Teacher-Student Interactions from a Feedback Perspective for Semi-supervised Medical Image Segmentation
The teacher-student paradigm has emerged as a canonical framework in semi-supervised learning. When applied to medical image segmentation, the paradigm faces challenges due to inherent image ambiguities, making it partic…
Semi-supervised Medical Image SegmentationCross-pyramid consistency regularization for semi-supervised medical image segmentation
Semi-supervised learning (SSL) enables training of powerful models with the assumption of limited, carefully labelled data and a large amount of unlabeled data to support the learning. In this paper, we propose a hybrid …
Semi-supervised Medical Image SegmentationSelf-Supervised LearningKnowledge Distillation