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/MiguelMonteiro/VNet-Tensorflow
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
https://github.com/Miltos-90/UNet_Biomedical_Image_Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Mind23-2/MindCode-101/tree/main/nnUNet
https://github.com/Mind23-2/MindCode-2/tree/main/ViG
ViG-UNet: Vision Graph Neural Networks for Medical Image Segmentation
https://github.com/Mind23-2/MindCode-2/tree/main/vnet
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
https://github.com/Mind23-2/MindCode-5/tree/main/PSPNet
https://github.com/Mind23-2/MindCode-5/tree/main/Pix2Pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Mind23-2/MindCode-61
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Mind23-2/MindCode-63
https://github.com/MindCode-4/code-2/tree/main/phi
https://github.com/MindCode-4/code-3/tree/main/dinov2
DINOv2 based Self Supervised Learning For Few Shot Medical Image Segmentation
https://github.com/MindCode-4/code-3/tree/main/encoder_decoder
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
https://github.com/MindCode-4/code-7/tree/main/matthews-correlation
https://github.com/MindCode-4/code-9/tree/main/smu
SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities
https://github.com/MindSpore-MS-Code2/code0/tree/main/DRNet
https://github.com/MindSpore-paper-code-2/code2/tree/main/PSPNet
https://github.com/MindSpore-paper-code-2/code2/tree/main/Pix2Pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/MindSpore-paper-code-2/code2/tree/main/nnUNet
nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
https://github.com/MindSpore-paper-code-2/code3/tree/main/UNet3%2B
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
https://github.com/MindSpore-paper-code-2/code3/tree/main/tinydarknet
https://github.com/MindSpore-paper-code-2/code3/tree/main/vnet
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
https://github.com/MindSpore-paper-code-2/code400/tree/main/DeepLabV3P
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
https://github.com/MindSpore-paper-code-2/code400/tree/main/DeepLabv3
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
https://github.com/MindSpore-paper-code-3/code3/tree/main/faster_rcnn_dcn
https://github.com/MindSpore-paper-code-3/code7/tree/main/FCN8s
https://github.com/MindSpore-paper-code-3/code9/tree/main/MaskRCNN
https://github.com/MindSpore-scientific-2/code-10/tree/main/Uncertainty_Calibration_Object_Detection
Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation
https://github.com/MindSpore-scientific-2/code-4/tree/main/D-Unet_Mindspore
Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation
https://github.com/MindSpore-scientific-2/code-5/tree/main/D-Unet_Mindspore
UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation
https://github.com/MindSpore-scientific/code-10/tree/main/Uncertainty_Calibration_Object_Detection
Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation
https://github.com/MindSpore-scientific/code-13/tree/main/Translation-Invariant/model
Translation Consistent Semi-supervised Segmentation for 3D Medical Images
https://github.com/MindSpore-scientific/code-13/tree/main/Uncertainty_Calibration_Object_Detection
Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation
https://github.com/MindSpore-scientific/code-14/tree/main/segmentation-dataset
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/MindSpore-scientific/code-5/tree/main/smu
SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities
https://github.com/MindSpore-scientific/code-6/tree/main/matthews-correlation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.