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Anatomy
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/kabbas570/crocnet
공식 코드Crop and Couple: cardiac image segmentation using interlinked specialist networks
https://github.com/karolinezhy/defcornet
공식 코드https://github.com/kent0n-li/agmb-transformer
공식 코드https://github.com/kent0n-li/fgdm
공식 코드Zero-shot Medical Image Translation via Frequency-Guided Diffusion Models
https://github.com/kent0n-li/gt-u-net
공식 코드GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentation
https://github.com/kid-liet/reg-gan
공식 코드https://github.com/liamchalcroft/contrast-squared
공식 코드Unified 3D MRI Representations via Sequence-Invariant Contrastive Learning
https://github.com/lilachbarkat/morphgac-segmentation-gui
공식 코드Image translation of Ultrasound to Pseudo Anatomical Display by CycleGAN
https://github.com/lileitech/awesome-cardiac-digital-twins
공식 코드Solving the Inverse Problem of Electrocardiography for Cardiac Digital Twins: A Survey
https://github.com/lkshrsch/MultiPrior_Brain
공식 코드Segmentation of MRI head anatomy using deep volumetric networks and multiple spatial priors
https://github.com/lkshrsch/multiaxial_brain_segmenter
공식 코드Full-Head Segmentation of MRI with Abnormal Brain Anatomy: Model and Data Release
https://github.com/lorevansantvliet/biventricularssm
공식 코드https://github.com/lorifranke/FiberStars
공식 코드FiberStars: Visual Comparison of Diffusion Tractography Data between Multiple Subjects
https://github.com/lppllppl920/DenseDescriptorLearning-Pytorch
공식 코드Extremely Dense Point Correspondences using a Learned Feature Descriptor
https://github.com/lppllppl920/DenseReconstruction-Pytorch
공식 코드https://github.com/lppllppl920/EndoscopyDepthEstimation-Pytorch
공식 코드Dense Depth Estimation in Monocular Endoscopy with Self-supervised Learning Methods
https://github.com/lppllppl920/sage-slam
공식 코드https://github.com/lucasmansilla/ACRN_Chest_X-ray_IA
공식 코드Learning Deformable Registration of Medical Images with Anatomical Constraints
https://github.com/lucasmansilla/NIH_chest_xray14_segmentations
공식 코드Learning Deformable Registration of Medical Images with Anatomical Constraints
https://github.com/luo-yihao/ghdheart
공식 코드Explicit Differentiable Slicing and Global Deformation for Cardiac Mesh Reconstruction
https://github.com/lyh-hh/afire
공식 코드https://github.com/lyxdlii/procns
공식 코드https://github.com/m1balcerak/PhysRegTumor
공식 코드Physics-Regularized Multi-Modal Image Assimilation for Brain Tumor Localization
https://github.com/m3dv/impulse
공식 코드Template-Guided Reconstruction of Pulmonary Segments with Neural Implicit Functions
https://github.com/mabulnaga/placenta-flattening
공식 코드https://github.com/mahehu/TUT-live-age-estimator
공식 코드https://github.com/malikteng/morphinetv2
공식 코드MorphiNet: A Graph Subdivision Network for Adaptive Bi-ventricle Surface Reconstruction
https://github.com/mareikethies/geometry_gradients_ct
공식 코드A gradient-based approach to fast and accurate head motion compensation in cone-beam CT
https://github.com/marlin-codes/HIE
공식 코드Hyperbolic Graph Neural Networks: A Review of Methods and Applications
https://github.com/masilab/da_ct_muscle_seg
공식 코드Single Slice Thigh CT Muscle Group Segmentation with Domain Adaptation and Self-Training
https://github.com/matonima/lightweight-classifier/blob/main/test_model_2.py
공식 코드Lightweight Combinational Machine Learning Algorithm for Sorting Canine Torso Radiographs
https://github.com/mazurowski-lab/foundation-based-reg
공식 코드Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?
https://github.com/med-air/interactive-registration
공식 코드https://github.com/medimair/brain-mri-processing-pipeline
공식 코드Negligible effect of brain MRI data preprocessing for tumor segmentation
https://github.com/megdec/vascularmd
공식 코드Modeling and hexahedral meshing of cerebral arterial networks from centerlines
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.