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Semi-supervised Medical Image Segmentation

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하위 태스크: 0
평가표: 7
보존된 평가 기록: 18

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/leesoon1984/cmmt-net

공식 코드

Cross-head mutual Mean-Teaching for semi-supervised medical image segmentation

https://github.com/leiluk1/gaze-based-segmentation

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Gaze-Assisted Medical Image Segmentation

https://github.com/lzeeorno/lagrange-duality-and-cmaformer

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Lagrange Duality and Compound Multi-Attention Transformer for Semi-Supervised Medical Image Segmentation

https://github.com/maxwell0027/lefed

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Discrepancy Matters: Learning from Inconsistent Decoder Features for Consistent Semi-supervised Medical Image Segmentation

https://github.com/michaelvorndran/inconsistencymasks

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Inconsistency Masks: Removing the Uncertainty from Input-Pseudo-Label Pairs

https://github.com/moucheng2017/emssl

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Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi-Supervised Segmentation

https://github.com/moucheng2017/mismatchssl

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MisMatch: Calibrated Segmentation via Consistency on Differential Morphological Feature Perturbations with Limited Labels

https://github.com/mqinghe/midss

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Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation

https://github.com/mqinghe/synfoc

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Steady Progress Beats Stagnation: Mutual Aid of Foundation and Conventional Models in Mixed Domain Semi-Supervised Medical Image Segmentation

https://github.com/mqinghe/ust-run

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Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation

https://github.com/ortonwang/plgdf

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Pseudo Label-Guided Data Fusion and Output Consistency for Semi-Supervised Medical Image Segmentation

https://github.com/pascalcpp/sdcl

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SDCL: Students Discrepancy-Informed Correction Learning for Semi-supervised Medical Image Segmentation

https://github.com/qiumuyang/siab

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The Devil is in the Statistics: Mitigating and Exploiting Statistics Difference for Generalizable Semi-supervised Medical Image Segmentation

https://github.com/rohit-kundu/ideal-icassp23

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IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image Segmentation

https://github.com/rows21/dk-uxnet

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Semi-supervised Medical Image Segmentation via Query Distribution Consistency

https://github.com/ruizhe-l/semi-segmentation

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A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation

https://github.com/senyh/ucmt

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Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation

https://github.com/shinkaiz/clcc-semi

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Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image Segmentation

https://github.com/shumengli/cfr

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Stitching, Fine-tuning, Re-training: A SAM-enabled Framework for Semi-supervised 3D Medical Image Segmentation

https://github.com/shumengli/sfr

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Stitching, Fine-tuning, Re-training: A SAM-enabled Framework for Semi-supervised 3D Medical Image Segmentation

https://github.com/sichengs/manet

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Manifold-Aware Local Feature Modeling for Semi-Supervised Medical Image Segmentation

https://github.com/taozh2017/decseg

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Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation

https://github.com/taozh2017/knowsam

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Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation

https://github.com/wutcm-lab/shape-prior-semi-seg

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Striving for Simplicity: Simple Yet Effective Prior-Aware Pseudo-Labeling for Semi-Supervised Ultrasound Image Segmentation

https://github.com/xmed-lab/dhc

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DHC: Dual-debiased Heterogeneous Co-training Framework for Class-imbalanced Semi-supervised Medical Image Segmentation

https://github.com/xmeimeimei/sgtc

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SGTC: Semantic-Guided Triplet Co-training for Sparsely Annotated Semi-Supervised Medical Image Segmentation

https://github.com/y-tarl/c3s3

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C3S3: Complementary Competition and Contrastive Selection for Semi-Supervised Medical Image Segmentation

https://github.com/ycwu1997/mc-net

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Mutual Consistency Learning for Semi-supervised Medical Image Segmentation

https://github.com/ycwu1997/ss-net

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Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation

https://github.com/yichizhang98/semisam

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SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization

https://github.com/yyliu01/tracoco

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Translation Consistent Semi-supervised Segmentation for 3D Medical Images

https://github.com/zhenzhao/ad-mt

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Alternate Diverse Teaching for Semi-supervised Medical Image Segmentation

https://github.com/zhenzhao/dpms

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Rethinking Data Perturbation and Model Stabilization for Semi-supervised Medical Image Segmentation

https://github.com/zhuye98/icl

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Inherent Consistent Learning for Accurate Semi-supervised Medical Image Segmentation

https://github.com/ziyangwang007/cv-ssl-mis

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When CNN Meet with ViT: Towards Semi-Supervised Learning for Multi-Class Medical Image Semantic Segmentation

https://github.com/ziyangwang007/mamba-unet

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Semi-Mamba-UNet: Pixel-Level Contrastive and Pixel-Level Cross-Supervised Visual Mamba-based UNet for Semi-Supervised Medical Image Segmentation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.