https://github.com/ICT-MIRACLE-lab/YOLO_Universal_Anatomical_Landmark_Detection
공식 코드You Only Learn Once: Universal Anatomical Landmark Detection
Papers with Code 아카이브
Anatomical Landmark Detection
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/cardio-ai/suture-detection-pytorch
공식 코드Point detection through multi-instance deep heatmap regression for sutures in endoscopy
https://github.com/ecnuacrush/hyatt-net
공식 코드HYATT-Net is Grand: A Hybrid Attention Network for Performant Anatomical Landmark Detection
https://github.com/healthx-lab/cabld
공식 코드CABLD: Contrast-Agnostic Brain Landmark Detection with Consistency-Based Regularization
https://github.com/healthx-lab/camld
공식 코드CABLD: Contrast-Agnostic Brain Landmark Detection with Consistency-Based Regularization
https://github.com/jhb86253817/uda_med_landmark
공식 코드Unsupervised Domain Adaptation for Anatomical Landmark Detection
https://github.com/juvenileinwind/farnet
공식 코드Feature Aggregation and Refinement Network for 2D AnatomicalLandmark Detection
https://github.com/juzhengmiao/fm-osd
공식 코드FM-OSD: Foundation Model-Enabled One-Shot Detection of Anatomical Landmarks
https://github.com/miracle-center/yolo_universal_anatomical_landmark_detection
공식 코드You Only Learn Once: Universal Anatomical Landmark Detection
https://github.com/tanyelai/deep-breast-positioning
공식 코드Mammographic Breast Positioning Assessment via Deep Learning
https://github.com/arcadelab/DeepDRR
DeepDRR -- A Catalyst for Machine Learning in Fluoroscopy-guided Procedures
https://github.com/mathiasunberath/DeepDRR
DeepDRR -- A Catalyst for Machine Learning in Fluoroscopy-guided Procedures
https://github.com/tym002/landmark_detection
Volumetric landmark detection with a multi-scale shift equivariant neural network
https://github.com/yf817/ICNet
Inverse-Consistent Deep Networks for Unsupervised Deformable Image Registration
https://github.com/zhangjun001/ICNet
Inverse-Consistent Deep Networks for Unsupervised Deformable Image Registration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.