https://github.com/MASILab/SynSeg-Net
Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground Truth
Papers with Code 아카이브
Medical Image Segmentation
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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/MECLabTUDA/ACS
Importance Driven Continual Learning for Segmentation Across Domains
https://github.com/MECLabTUDA/M3d-Cam
https://github.com/MECLabTUDA/OOD-Gen
How Reliable Are Out-of-Distribution Generalization Methods for Medical Image Segmentation?
https://github.com/MIC-DKFZ/DetectionAndRegression
https://github.com/MIC-DKFZ/RegRCNN
https://github.com/MIMBCD-UI/avi-2020-short-paper
BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis
https://github.com/MIMBCD-UI/dataset-uta4-dicom
BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis
https://github.com/MIMBCD-UI/dataset-uta4-nasa-tlx
BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis
https://github.com/MIMBCD-UI/dataset-uta4-rates
BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis
https://github.com/MIMBCD-UI/dataset-uta4-sus
BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis
https://github.com/MIMBCD-UI/dataset-uta4-time
BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis
https://github.com/MS-Mind/MS-Code-06/tree/main/PSPNet
https://github.com/MS-Mind/MS-Code-06/tree/main/Pix2Pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/MS-Mind/MS-Code-06/tree/main/nnUNet
nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
https://github.com/MS-Mind/MS-Code-08/tree/main/MIMO-UNet
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
https://github.com/MS-Mind/MS-Code-08/tree/main/nnUNet
nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
https://github.com/MaczekO/AttentionNetworkProject
FocusNet: An attention-based Fully Convolutional Network for Medical Image Segmentation
https://github.com/Makunda/DeepLearningASL
https://github.com/MargeryLab/Pytorch-UNet-copy
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/MargeryLab/TransUNet
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
https://github.com/Markin-Wang/EARNet
EAR-NET: Error Attention Refining Network For Retinal Vessel Segmentation
https://github.com/MartinBuessemeyer/Artistic-Texture-Control
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/MarvinLavechin/imagetranslation-tensorflow
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Masaaki-75/proct
https://github.com/MasoumehVahedi/Brain_MRI_Segmentation
https://github.com/MasoumehVahedi/Pix2Pix-GAN-model
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/MatthewQi2002/Unet_mindspore
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Maveric4/pr19aaw01
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Maxencephilbert/In_Silico_Labeling_CBIO_internship
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Image_to_Image_Translation_with_Conditional_Adversarial_Nets
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Mayurji/Image-Classification-PyTorch
https://github.com/MdAli1993/Multiclass_Semantic_Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Media-Smart/vedaseg
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/MegEngine/Models/tree/master/official/vision/segmentation
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
https://github.com/MiRA-lab-dev/SynRec
FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.