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/aecelaya/pocketnet
PocketNet: A Smaller Neural Network for Medical Image Analysis
https://github.com/affinelayer/Pix2Pix-tensorflow
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/agassi4013/Log-DenseNet-Tensorflow
https://github.com/ahedayat/U-Net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ai-med/MAS-LR
Importance Driven Continual Learning for Segmentation Across Domains
https://github.com/ai-med/QuickNATv2
QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy
https://github.com/ai-med/quickNAT_pytorch
QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy
https://github.com/aigitsaj13/pix2pix-model
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/aijialin/style_transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/aimbsg/EVA4_S15
https://github.com/aixiaobaikkk/LUCF-Net
LUCF-Net: Lightweight U-shaped Cascade Fusion Network for Medical Image Segmentation
https://github.com/ajaystar8/UNet-PyTorch
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ajinas-ibrahim/brain_tumor
https://github.com/ajjdan/KaI
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ajjdan/Karst-Segmentation-from-DEM
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ajoshi944/Segmentation-severstal-steel
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ajoshiusc/qrsegment
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/akanametov/Pix2Pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/akhadangi/EM-net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/akrsrivastava/Unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/aksub99/U-Net-Pytorch
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/albarji/neurowriter
https://github.com/alberto-solano/drive-convmixer
https://github.com/albertsokol/pneumothorax-detection-unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/alejandrodebus/SegNet
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/alexalm4190/Mask_RCNN-Vizzy_Hand
https://github.com/alexander-pv/maskrcnn_tf2
https://github.com/alexandrelewin/FollowMe
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/alexbmp/run-vnet-keras
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
https://github.com/alexgkendall/SegNet-Tutorial
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/alexgkendall/caffe-segnet
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/alexjc/neural-enhance
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/alexklibisz/deep-calcium
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/alexromano/nerve-segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/alexshuang/TGS_Salt
Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.