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Semi-supervised Medical Image Segmentation
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation
C3S3: Complementary Competition and Contrastive Selection for Semi-Supervised Medical Image Segmentation
Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation
Decoupled Competitive Framework for Semi-supervised Medical Image Segmentation
Efficient Prototype Consistency Learning in Medical Image Segmentation via Joint Uncertainty and Data Augmentation
Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation
P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation
TransMedSeg: A Transferable Semantic Framework for Semi-Supervised Medical Image Segmentation
Rethinking the Mean Teacher Strategy from the Perspective of Self-paced Learning
WT-BCP: Wavelet Transform based Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation
Balancing Multi-Target Semi-Supervised Medical Image Segmentation with Collaborative Generalist and Specialists
Steady Progress Beats Stagnation: Mutual Aid of Foundation and Conventional Models in Mixed Domain Semi-Supervised Medical Image Segmentation
Semi-KAN: KAN Provides an Effective Representation for Semi-Supervised Learning in Medical Image Segmentation
Striving for Simplicity: Simple Yet Effective Prior-Aware Pseudo-Labeling for Semi-Supervised Ultrasound Image Segmentation
Boosting Semi-Supervised Medical Image Segmentation via Masked Image Consistency and Discrepancy Learning
Multi-Prototype Embedding Refinement for Semi-Supervised Medical Image Segmentation
GIGP: A Global Information Interacting and Geometric Priors Focusing Framework for Semi-supervised Medical Image Segmentation
Diff-CL: A Novel Cross Pseudo-Supervision Method for Semi-supervised Medical Image Segmentation
Semi-Supervised Medical Image Segmentation via Knowledge Mining from Large Models
Enhancing SAM with Efficient Prompting and Preference Optimization for Semi-supervised Medical Image Segmentation
SemiSAM+: Rethinking Semi-Supervised Medical Image Segmentation in the Era of Foundation Models
Bidirectional Uncertainty-Aware Region Learning for Semi-Supervised Medical Image Segmentation
CAD: Confidence-Aware Adaptive Displacement for Semi-Supervised Medical Image Segmentation
FedSemiDG: Domain Generalized Federated Semi-supervised Medical Image Segmentation
SGTC: Semantic-Guided Triplet Co-training for Sparsely Annotated Semi-Supervised Medical Image Segmentation
Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation
Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation
DuSSS: Dual Semantic Similarity-Supervised Vision-Language Model for Semi-Supervised Medical Image Segmentation
A SAM-guided and Match-based Semi-Supervised Segmentation Framework for Medical Imaging
GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation
DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation
SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation
Synergy-Guided Regional Supervision of Pseudo Labels for Semi-Supervised Medical Image Segmentation
Double Banking on Knowledge: Customized Modulation and Prototypes for Multi-Modality Semi-supervised Medical Image Segmentation
Gaze-Assisted Medical Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.