https://github.com/key1589745/decouple_predict
공식 코드Decoupling Predictions in Distributed Learning for Multi-Center Left Atrial MRI Segmentation
Papers with Code 아카이브
MRI segmentation
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/lhaof/asa
공식 코드https://github.com/lhaof/asc
공식 코드https://github.com/linxuanhan/e-mim
공식 코드Enhanced Masked Image Modeling to Avoid Model Collapse on Multi-modal MRI Datasets
https://github.com/linxuanhan/m2-mae
공식 코드Enhanced Masked Image Modeling to Avoid Model Collapse on Multi-modal MRI Datasets
https://github.com/liuquande/SAML
공식 코드Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains
https://github.com/lkshrsch/multiaxial_brain_segmenter
공식 코드Full-Head Segmentation of MRI with Abnormal Brain Anatomy: Model and Data Release
https://github.com/lucasfidon/trustworthy-ai-fetal-brain-segmentation
공식 코드A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation
https://github.com/mayurmallya/deepguide
공식 코드https://github.com/mazurowski-lab/segmentanybone
공식 코드SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI
https://github.com/med-air/dltta
공식 코드DLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical Images
https://github.com/miagrouput/augmentations-for-the-unknown
공식 코드https://github.com/mirthai/csa-net
공식 코드A Flexible 2.5D Medical Image Segmentation Approach with In-Slice and Cross-Slice Attention
https://github.com/neerakara/tta-1d-density-matching
공식 코드A Field of Experts Prior for Adapting Neural Networks at Test Time
https://github.com/neuro-ml/domain_shift_anatomy
공식 코드Anatomy of Domain Shift Impact on U-Net Layers in MRI Segmentation
https://github.com/nubagcilab/pansegnet
공식 코드Large-Scale Multi-Center CT and MRI Segmentation of Pancreas with Deep Learning
https://github.com/oliverjm1/bmvc_menisc_seg
공식 코드https://github.com/razaimam45/ai701-project-transfer-learning-approach-for-imbalance-classification-of-brain-tumor-mri-
공식 코드https://github.com/reeive/rehydil
공식 코드https://github.com/robert-graf/totalvibesegmentator
공식 코드TotalVibeSegmentator: Full Body MRI Segmentation for the NAKO and UK Biobank
https://github.com/rohit-kundu/ideal-icassp23
공식 코드IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image Segmentation
https://github.com/romainvala/synthetic_learning_on_dhcp
공식 코드Comprehensive analysis of synthetic learning applied to neonatal brain MRI segmentation
https://github.com/russelljeffrey/FedBrain-Distill
공식 코드https://github.com/scott-yjyang/hcdg
공식 코드HCDG: A Hierarchical Consistency Framework for Domain Generalization on Medical Image Segmentation
https://github.com/shengkuangcn/mscda
공식 코드https://github.com/simonzhou86/en_dran
공식 코드Edge-Enhanced Dilated Residual Attention Network for Multimodal Medical Image Fusion
https://github.com/sotiraslab/QCResUNet
공식 코드QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality Prediction
https://github.com/tbuikr/3D_DenseSeg
공식 코드3D Densely Convolutional Networks for Volumetric Segmentation
https://github.com/tiffany9056/uu-mamba
공식 코드UU-Mamba: Uncertainty-aware U-Mamba for Cardiac Image Segmentation
https://github.com/tuat-novice/maprotonet
공식 코드https://github.com/virginiafdez/brainspade3d_rel
공식 코드A 3D generative model of pathological multi-modal MR images and segmentations
https://github.com/wasserth/totalsegmentator
공식 코드https://github.com/xmed-lab/ddaug
공식 코드Dynamic Data Augmentation via MCTS for Prostate MRI Segmentation
https://github.com/zenghy96/reliable-source-approximation
공식 코드https://github.com/zhenxuan-zhang/tucl_brainseg
공식 코드Task-oriented Uncertainty Collaborative Learning for Label-Efficient Brain Tumor Segmentation
https://github.com/BBillot/hypothalamus_seg
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.