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/scut-cszcl/sfusion
공식 코드SFusion: Self-attention based N-to-One Multimodal Fusion Block
https://github.com/scut-ml-guo/mimo
공식 코드Evaluation of Multi-indicator And Multi-organ Medical Image Segmentation Models
https://github.com/scverse/scvi-tools
공식 코드https://github.com/seedatnabeel/Uncertainty-Decision-Support-Healthcare
공식 코드https://github.com/selgroup/sled
공식 코드https://github.com/senyh/ucmt
공식 코드Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation
https://github.com/sepidehamiri/pet_seg_unet
공식 코드Improved automated lesion segmentation in whole-body FDG/PET-CT via Test-Time Augmentation
https://github.com/sergiormpereira/rr_segse
공식 코드https://github.com/sfu-mial/skin-lesion-segmentation-survey
공식 코드https://github.com/sfu-mial/styleseg
공식 코드Segmentation Style Discovery: Application to Skin Lesion Images
https://github.com/sh4174/hypernetensemble
공식 코드https://github.com/sha168/ADNet
공식 코드https://github.com/shahinakk/lg_sdg
공식 코드Language Guided Domain Generalized Medical Image Segmentation
https://github.com/shahinakk/lwi-vms
공식 코드Learnable Weight Initialization for Volumetric Medical Image Segmentation
https://github.com/shanzha09/COIPS
공식 코드https://github.com/shaoshitong/DiffuseExpand
공식 코드DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion models
https://github.com/sharibox/EndoCV2021-polyp_det_seg_gen
공식 코드A multi-centre polyp detection and segmentation dataset for generalisability assessment
https://github.com/sharibox/polypgen-benchmark
공식 코드A multi-centre polyp detection and segmentation dataset for generalisability assessment
https://github.com/sharifamit/swin-vftr
공식 코드SwinVFTR: A Novel Volumetric Feature-learning Transformer for 3D OCT Fluid Segmentation
https://github.com/sharifmhamza/transresnet
공식 코드https://github.com/shashankhuang/reclmis
공식 코드Cross-Modal Conditioned Reconstruction for Language-guided Medical Image Segmentation
https://github.com/shawnhuang497/reclmis
공식 코드Cross-Modal Conditioned Reconstruction for Language-guided Medical Image Segmentation
https://github.com/shengfly/protoseg
공식 코드Segmentation Ability Map: Interpret deep features for medical image segmentation
https://github.com/shengkuangcn/mscda
공식 코드https://github.com/shengyu27/crackmamba
공식 코드https://github.com/shijun18/paal-medseg
공식 코드Predictive Accuracy-Based Active Learning for Medical Image Segmentation
https://github.com/shinkaiz/clcc-semi
공식 코드https://github.com/shishuaihu/ccsdg
공식 코드Devil is in Channels: Contrastive Single Domain Generalization for Medical Image Segmentation
https://github.com/shishuaihu/prosfda
공식 코드ProSFDA: Prompt Learning based Source-free Domain Adaptation for Medical Image Segmentation
https://github.com/shjo-april/coin
공식 코드COIN: Confidence Score-Guided Distillation for Annotation-Free Cell Segmentation
https://github.com/shu-hai/two-stage-VAE-Attention-gate-BraTS2020
공식 코드https://github.com/shuaichenbigr/label-refinement-network
공식 코드Label Refinement Network from Synthetic Error Augmentation for Medical Image Segmentation
https://github.com/shubhaminnani/egan
공식 코드Generative Adversarial Networks based Skin Lesion Segmentation
https://github.com/shumengli/cfr
공식 코드https://github.com/shumengli/sfr
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.