Papers with Code 아카이브
Brain Segmentation
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<span style="color:grey; opacity: 0.6">( Image credit: 3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mateuszbuda/brain-segmentation
공식 코드https://github.com/microsoft/voxhrnet
공식 코드https://github.com/mostafa-ghazi/fast-aid-brain
공식 코드https://github.com/mostafa-ghazi/mri-augmentation
공식 코드https://github.com/neuronets/kwyk
공식 코드Knowing what you know in brain segmentation using Bayesian deep neural networks
https://github.com/peirong26/Brain-ID
공식 코드Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging
https://github.com/romainvala/synthetic_learning_on_dhcp
공식 코드Comprehensive analysis of synthetic learning applied to neonatal brain MRI segmentation
https://github.com/tbdair/fedperlv1.0
공식 코드ROAM: Random Layer Mixup for Semi-Supervised Learning in Medical Imaging
https://github.com/tbuikr/3D_DenseSeg
공식 코드3D Densely Convolutional Networks for Volumetric Segmentation
https://github.com/timothy102/Brain-FMRI
공식 코드AGD-Autoencoder: Attention Gated Deep Convolutional Autoencoder for Brain Tumor Segmentation
https://github.com/urrand/perinatal-pipeline
공식 코드An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis
https://zenodo.org/record/15255556
공식 코드GOUHFI: a novel contrast- and resolution-agnostic segmentation tool for Ultra-High Field MRI
https://github.com/BBillot/hypothalamus_seg
https://github.com/BBillot/lab2im
https://github.com/BaiqingL/CS523-Final
https://github.com/CaelynCheung1996/QuickNAT_tensorflow_V2
QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy
https://github.com/FNNDSC/pl-surfaces-fetus
https://github.com/Gjiangtao/A-Deep-Supervised-Edge-Optimization-Algorithm-for-Salt-Body-Segmentation
Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks
https://github.com/K-Mike/Automatic-salt-deposits-segmentation
Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks
https://github.com/MASILab/SLANT_brain_seg
Spatially Localized Atlas Network Tiles Enables 3D Whole Brain Segmentation from Limited Data
https://github.com/MECLabTUDA/ACS
Importance Driven Continual Learning for Segmentation Across Domains
https://github.com/MECLabTUDA/M3d-Cam
https://github.com/SenthilCaesar/CNN-Brain-MRI-Segmentation
QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy
https://github.com/WTCN-computational-anatomy-group/MTVprocess3D
https://github.com/abhi4ssj/BayesianQuickNAT
https://github.com/abhi4ssj/squeeze_and_excitation
Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks
https://github.com/ai-med/MAS-LR
Importance Driven Continual Learning for Segmentation Across Domains
https://github.com/ai-med/QuickNATv2
QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy
https://github.com/ai-med/quickNAT_pytorch
QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy
https://github.com/alexshuang/TGS_Salt
Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks
https://github.com/ashishpatel26/BrainMRI-Segmentation-Keras
https://github.com/avinash0309/BRAIN-TUMOR-SEGEMENTATION-OF-LOW-GRADE-GLIOMAS
https://github.com/balbasty/super-resolution
https://github.com/bclwan/MRI_Brain_Segmentation
https://github.com/black0017/MedicalZooPytorch
HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.