Papers with Code 아카이브
Medical Image Segmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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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/nashawnch/ComputerVision-FerrariDetective
https://github.com/navganti/SIVO
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/navganti/SegNet
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/navidhasanzadeh/UNet-Semantic-Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/nbouteldja/kidneystaintranslation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/neerakara/TTA_prostate
Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation
https://github.com/nekitmm/starnet
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/nellish123/contrib
D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation
https://github.com/neptune-ai/open-solution-mapping-challenge
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/neshitov/UNet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/neuropoly/multiclass-segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/nidebroux/lumbosacral_segmentation
https://github.com/nikhilroxtomar/Deep-Residual-Unet
https://github.com/nikhilroxtomar/UNet-Segmentation-in-Keras-TensorFlow
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/nikhilroxtomar/Unet-for-Person-Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/nikhilroxtomar/semantic-segmentation-architecture
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/nikhithakarennagari/1311
https://github.com/nikhithakarennagari/Git
https://github.com/nikunj-taneja/accendo
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/ninanikitina/BioLab
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ninatu/mood_challenge
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/niranjana98/Image-Classification
https://github.com/nishantcoder97/cgandemo
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/nisharaichur/SegNet-Encoder-Decoder-Architecture-for-Image-Segmentation
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/nisharaichur/segNet_tensorflow
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/nithi89/unet_darknet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/nnoyuwan/CAR-UNet-master
Channel Attention Residual U-Net for Retinal Vessel Segmentation
https://github.com/nnzzll/networks
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
https://github.com/noahfl/densenet-sdr
https://github.com/noelcodes/Mask_RCNN
https://github.com/nolanliou/PeopleSegmentationDemo
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
https://github.com/noornk/U-Net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/noufali/VideoML
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/o-evgeny/MRCNN_DeepFashion2
https://github.com/oakleighw/BRATS-Pretext-Tasks-for-Self-supervised-Deep-Learning
U-Net: Convolutional Networks for Biomedical Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.