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Brain Tumor Segmentation

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평가표: 12
보존된 평가 기록: 27

Brain Tumor Segmentation is a medical image analysis task that involves the separation of brain tumors from normal brain tissue in magnetic resonance imaging (MRI) scans. The goal of brain tumor segmentation is to produce a binary or multi-class segmentation map that accurately reflects the location and extent of the tumor.

<span style="color:grey; opacity: 0.6">( Image credit: Brain Tumor Segmentation with Deep Neural Networks )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/SWKoreaBME/sangwook_kits2019

Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge

https://github.com/Shrajan/AAAI-2022

Attention U-Net: Learning Where to Look for the Pancreas

https://github.com/Shrajan/IB_U_Nets

Attention U-Net: Learning Where to Look for the Pancreas

https://github.com/SonwYang/building-extraction

Attention U-Net: Learning Where to Look for the Pancreas

https://github.com/Spider-scnu/Instance-Segmentation-For-Cancer

Attention U-Net: Learning Where to Look for the Pancreas

https://github.com/SzuPc/mindspore-UnetAG

Attention U-Net: Learning Where to Look for the Pancreas

https://github.com/TheInfamousWayne/UNet

Attention U-Net: Learning Where to Look for the Pancreas

https://github.com/WouterDurnez/airhead

nnU-Net for Brain Tumor Segmentation

https://github.com/YuanXue1993/SegAN

SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image Segmentation

https://github.com/abhi134/Brain_Tumor_Segmentation

Brain Tumor Segmentation with Deep Neural Networks

https://github.com/aimansnigdha/ambiguous-medical-image-segmentation-using-diffusion-models

Diffusion Models for Implicit Image Segmentation Ensembles

https://github.com/ajinas-ibrahim/brain_tumor

Brain Tumor Segmentation with Deep Neural Networks

https://github.com/angadbajwa23/Brain-Image-Segmentation

Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge

https://github.com/angadbajwa23/Brain-Tumor-Segmentation

Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge

https://github.com/angadbajwa23/Segmentation-of-2D-Brain-MR-Images-using-Deep-Neural-Architectures

Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge

https://github.com/aparecidovieira/Road_extraction

Attention U-Net: Learning Where to Look for the Pancreas

https://github.com/athon2/BraTS2018_NvNet

3D MRI brain tumor segmentation using autoencoder regularization

https://github.com/avinash0309/BRAIN-TUMOR-SEGEMENTATION-OF-LOW-GRADE-GLIOMAS

Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm

https://github.com/ayoub-sudo/Attention-Unet-web-app

Attention U-Net: Learning Where to Look for the Pancreas

https://github.com/bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets

Attention U-Net: Learning Where to Look for the Pancreas

https://github.com/black0017/MedicalZooPytorch

3D MRI brain tumor segmentation using autoencoder regularization

https://github.com/bo-10000/VoxResNet

Attention U-Net: Learning Where to Look for the Pancreas

https://github.com/charan223/Brain-Tumor-Segmentation-using-Topological-Loss

Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks

https://github.com/charan223/brain_tumor_topology

Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks

https://github.com/charan223/topology-aware-networks

Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks

https://github.com/charan223/topology-conscious-networks

Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks

https://github.com/christophbrgr/brats-orchestra

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

https://github.com/cocofeat/devis

TBraTS: Trusted Brain Tumor Segmentation

https://github.com/cocofeat/umis

TBraTS: Trusted Brain Tumor Segmentation

https://github.com/cswin/AWC

Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis

https://github.com/dijju/mri-cnn

Brain Tumor Segmentation with Deep Neural Networks

https://github.com/etjoa003/medical_imaging

Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation

https://github.com/fhaghighi/SemanticGenesis

Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration

https://github.com/gallegi/T4E_MICCAI_BrainTumor

The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

https://github.com/ge-xing/segmamba

The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

https://github.com/iNLyze/DeepLearning-SeGAN-Segmentation

SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image Segmentation

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