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Tumor Segmentation
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Tumor Segmentation is the task of identifying the spatial location of a tumor. It is a pixel-level prediction where each pixel is classified as a tumor or background. The most popular benchmark for this task is the BraTS dataset. The models are typically evaluated with the Dice Score metric.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yaziciz/GLIMS
공식 코드Attention-Enhanced Hybrid Feature Aggregation Network for 3D Brain Tumor Segmentation
https://github.com/yejin0111/autopet2022_blackbean
공식 코드Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT Scans
https://github.com/yigepeng/autopet_false_positive_reduction
공식 코드https://github.com/yousefis/DenseUnet_Esophagus_Segmentation
공식 코드Esophageal Tumor Segmentation in CT Images using Dilated Dense Attention Unet (DDAUnet)
https://github.com/ysmei97/multimodal_pci_mask
공식 코드Exploiting Partial Common Information Microstructure for Multi-Modal Brain Tumor Segmentation
https://github.com/yyliu01/tracoco
공식 코드Translation Consistent Semi-supervised Segmentation for 3D Medical Images
https://github.com/zhenxuan-zhang/tucl_brainseg
공식 코드Task-oriented Uncertainty Collaborative Learning for Label-Efficient Brain Tumor Segmentation
https://github.com/zhongaobo/asymmetry-bts
공식 코드https://github.com/zhoul-lab/plhn
공식 코드Prototype Learning Guided Hybrid Network for Breast Tumor Segmentation in DCE-MRI
https://github.com/zinic95/HFTrans
공식 코드https://github.com/zongzi3zz/cat
공식 코드CAT: Coordinating Anatomical-Textual Prompts for Multi-Organ and Tumor Segmentation
https://github.com/zywoosimple/rt-srts
공식 코드https://github.com/zzzsn/miccai2022-autopet
공식 코드https://github.com/2024-MindSpore-1/Code7/tree/main/convmixer
CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network
https://github.com/AchintyaX/Brain_tumor_segmentation
https://github.com/AryaKoureshi/Brain-tumor-detection
https://github.com/BaiqingL/CS523-Final
https://github.com/BecauseOfGAN/capston
https://github.com/BecauseOfGAN/capstone
https://github.com/Beckschen/TransUNet
3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers
https://github.com/Deeksha-5/STAN_Pytorch_Implementation
Stan: Small tumor-aware network for breast ultrasound image segmentation
https://github.com/EverLookNeverSee/Optimized-U-Net
https://github.com/Exclusivexc/CLISC
CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentation
https://github.com/Fabienne703/Data
Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks
https://github.com/FelixGruen/tensorflow-u-net
https://github.com/GinZhu/MIASSR
https://github.com/GinZhu/RDST
https://github.com/HimBegginer/test_liver
https://github.com/Huiyu-Li/Three-stage-Curriculum-Learning
A New Three-stage Curriculum Learning Approach to Deep Network Based Liver Tumor Segmentation
https://github.com/IAmSuyogJadhav/3d-mri-brain-tumor-segmentation-using-autoencoder-regularization
3D MRI brain tumor segmentation using autoencoder regularization
https://github.com/IAmSuyogJadhav/Brainy
https://github.com/IE-group/conference_report
3D MRI brain tumor segmentation using autoencoder regularization
https://github.com/Jack-Etheredge/Brain-Tumor-Segmentation-3D-UNet-CNN
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge
https://github.com/JaouadT/STAN_implementation_pytorch
Stan: Small tumor-aware network for breast ultrasound image segmentation
https://github.com/Kaz-K/robust-glioma-segmentation
Robust Semantic Segmentation of Brain Tumor Regions from 3D MRIs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.