https://github.com/MIC-DKFZ/nnunet
공식 코드nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
Papers with Code 아카이브
Segmentation
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MICV-yonsei/EAGLE
공식 코드EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation
https://github.com/MIPT-Oulu/RobustCartilageSegmentation
공식 코드https://github.com/MIPT-Oulu/mCTSegmentation
공식 코드https://github.com/MJJdG/ULS23
공식 코드https://github.com/MMCheng/SSPC-Net
공식 코드SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Network
https://github.com/MSiam/AdaptiveMaskedProxies
공식 코드https://github.com/MSiam/TFSegmentation
공식 코드https://github.com/MSiam/motion_adaptation
공식 코드https://github.com/MSiam/video_class_agnostic_segmentation
공식 코드Video Class Agnostic Segmentation with Contrastive Learning for Autonomous Driving
https://github.com/MVIG-SJTU/WSHP
공식 코드Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer
https://github.com/MansoorehMontazerin/Livelayer-Software
공식 코드https://github.com/MarcBotet/hamlet
공식 코드To Adapt or Not to Adapt? Real-Time Adaptation for Semantic Segmentation
https://github.com/Marie0909/AtrialJSQnet
공식 코드https://github.com/MarielleJansen/Liver-segmentation
공식 코드https://github.com/MarvinLer/tcga_segmentation
공식 코드Weakly supervised multiple instance learning histopathological tumor segmentation
https://github.com/MarvinTeichmann/ConvCRF
공식 코드https://github.com/MarvinTeichmann/MultiNet
공식 코드MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
https://github.com/MaxwellEng/MICCAI_CHANLLENGE24_HJL
공식 코드https://github.com/McGregorWwww/UDTransNet
공식 코드https://github.com/Medical-Image-Analysis/LightReSeg
공식 코드Light-weight Retinal Layer Segmentation with Global Reasoning
https://github.com/Megvii-BaseDetection/DisAlign
공식 코드Distribution Alignment: A Unified Framework for Long-tail Visual Recognition
https://github.com/MegviiDetection/video_analyst
공식 코드https://github.com/MendelXu/ANN
공식 코드Asymmetric Non-local Neural Networks for Semantic Segmentation
https://github.com/Meowuu7/AutoGPart
공식 코드AutoGPart: Intermediate Supervision Search for Generalizable 3D Part Segmentation
https://github.com/Merrical/PADL
공식 코드Modeling Annotator Preference and Stochastic Annotation Error for Medical Image Segmentation
https://github.com/Merrical/TAB
공식 코드Transformer-based Annotation Bias-aware Medical Image Segmentation
https://github.com/MhLiao/DB
공식 코드Real-time Scene Text Detection with Differentiable Binarization
https://github.com/Mhaiyang/CVPR2021_PFNet
공식 코드https://github.com/MichaelFan01/STDC-Seg
공식 코드https://github.com/MiguelMonteiro/CRFasRNNLayer
공식 코드Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging Segmentation
https://github.com/MiguelMonteiro/permutohedral_lattice
공식 코드Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging Segmentation
https://github.com/MinaJf/DRU-net
공식 코드DRU-net: An Efficient Deep Convolutional Neural Network for Medical Image Segmentation
https://github.com/MinaJf/FU-net
공식 코드FU-net: Multi-class Image Segmentation Using Feedback Weighted U-net
https://github.com/MinghanLi/STMask
공식 코드Spatial Feature Calibration and Temporal Fusion for Effective One-stage Video Instance Segmentation
https://github.com/MingiJi/FRSKD
공식 코드Refine Myself by Teaching Myself: Feature Refinement via Self-Knowledge Distillation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.