Papers with Code 아카이브
Medical Image Segmentation
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- 평가표
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Medical Image Segmentation is a computer vision task that involves dividing an medical image into multiple segments, where each segment represents a different object or structure of interest in the image. The goal of medical image segmentation is to provide a precise and accurate representation of the objects of interest within the image, typically for the purpose of diagnosis, treatment planning, and quantitative analysis.
<span style="color:grey; opacity: 0.6">( Image credit: IVD-Net )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/HiLab-git/MIDeepSeg
https://github.com/HimBegginer/test_liver
https://github.com/Holmes1204/PyTorch_Unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Hsankesara/DeepResearch
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/HzFu/EyeQ_Enhancement
https://github.com/HzFu/MNet_DeepCDR
CE-Net: Context Encoder Network for 2D Medical Image 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/IMvision12/keras-vision-models
https://github.com/INOS-soft/AI
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/INOS-soft/Bro-coil
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/INOS-soft/KuE.electo-waffle
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/INOS-soft/Python-Tools
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/IRVLab/SUIM
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/IRVLab/SUIM-Net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/IVRL/w2s
Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders
https://github.com/IgorZaton/Mini-Projects
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/IlliaOvcharenko/lung-segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ImagingLab/Colorizing-with-GANs
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/IsaacPatole/TGS-Salt-Identification-Challenge
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/J-jjj7230/u-net_model
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/JHLee0513/Salt_detection_challenge
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/JTan2231/DenseNet
https://github.com/Jack-Etheredge/Brain-Tumor-Segmentation-3D-UNet-CNN
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/JanMarcelKezmann/TensorFlow-Advanced-Segmentation-Models
https://github.com/JanOPedersen/U-Net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/JanPalasek/resunet-pp-tensorflow
ResUNet++: An Advanced Architecture for Medical Image Segmentation
https://github.com/Janetteeeeeeee/double_unet
DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation
https://github.com/Jeongseungwoo/Fusion-net
FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics
https://github.com/Jeongseungwoo/U_net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/JifeiWang-WHU/Pytorch_Building_extraction
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/JinkuiH/WRB-Net
https://github.com/JinyongJeong/DeeplabV3_Apolloscape_and_CityScape
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
https://github.com/Jo-dsa/SemanticSeg
U-Net: Convolutional Networks for Biomedical Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.