Papers with Code 아카이브

Medical Image Segmentation

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하위 태스크: 31
평가표: 49
보존된 평가 기록: 332

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/ymp5078/semantic-stacking

공식 코드

S2S2: Semantic Stacking for Robust Semantic Segmentation in Medical Imaging

https://github.com/yonghengsun1997/odada

공식 코드

Medical Image Segmentation with Domain Adaptation: A Survey

https://github.com/yore0/ttdg-mgm

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Test-Time Domain Generalization via Universe Learning: A Multi-Graph Matching Approach for Medical Image Segmentation

https://github.com/ysmei97/multimodal_pci_mask

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Exploiting Partial Common Information Microstructure for Multi-Modal Brain Tumor Segmentation

https://github.com/yuan-12138/mi-segnet

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MI-SegNet: Mutual Information-Based US Segmentation for Unseen Domain Generalization

https://github.com/yucornetto/CAKES

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CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D Networks

https://github.com/yuexin18/morseg-cam-sam

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Morphology-Enhanced CAM-Guided SAM for weakly supervised Breast Lesion Segmentation

https://github.com/yuhoo0302/segment-anything-model-for-medical-images

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Segment Anything Model for Medical Images?

https://github.com/yuhui-zh15/transseg

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Adapting Pre-trained Vision Transformers from 2D to 3D through Weight Inflation Improves Medical Image Segmentation

https://github.com/yulequan/UA-MT

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Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation

https://github.com/yurongchen1998/yurong-lib

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Towards to Robust and Generalized Medical Image Segmentation Framework

https://github.com/yussef93/knowwhattolabel

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Knowing What to Label for Few Shot Microscopy Image Cell Segmentation

https://github.com/yuwenlo/hardnet-dfus

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HarDNet-DFUS: An Enhanced Harmonically-Connected Network for Diabetic Foot Ulcer Image Segmentation and Colonoscopy Polyp Segmentation

https://github.com/yyNoBug/VAE_segmentation

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Unsupervised Domain Adaptation through Shape Modeling for Medical Image Segmentation

https://github.com/yykzjh/pmfsnet

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PMFSNet: Polarized Multi-scale Feature Self-attention Network For Lightweight Medical Image Segmentation

https://github.com/yyliu01/tracoco

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Translation Consistent Semi-supervised Segmentation for 3D Medical Images

https://github.com/yzhn16/imos

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A Foundation Model for General Moving Object Segmentation in Medical Images

https://github.com/zang0902/EM-Net

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EM-Net: Efficient Channel and Frequency Learning with Mamba for 3D Medical Image Segmentation

https://github.com/zefanyang/pacingpseudo

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Non-Iterative Scribble-Supervised Learning with Pacing Pseudo-Masks for Medical Image Segmentation

https://github.com/zenghy96/reliable-source-approximation

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Reliable Source Approximation: Source-Free Unsupervised Domain Adaptation for Vestibular Schwannoma MRI Segmentation

https://github.com/zerojumpline/jcsaugment

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Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation

https://github.com/zfdong-code/MNet_MindSpore

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MNet: Rethinking 2D/3D Networks for Anisotropic Medical Image Segmentation

https://github.com/zhangxinrubit/carvemix

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CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation

https://github.com/zhangxinrubit/mome

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A Foundation Model for Brain Lesion Segmentation with Mixture of Modality Experts

https://github.com/zhaohaoyu376/morestyle

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MoreStyle: Relax Low-frequency Constraint of Fourier-based Image Reconstruction in Generalizable Medical Image Segmentation

https://github.com/zhaowenzhao/wmcsfb

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Efficient 3D affinely equivariant CNNs with adaptive fusion of augmented spherical Fourier-Bessel bases

https://github.com/zhaowenzhao/wvms

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Efficient 3D affinely equivariant CNNs with adaptive fusion of augmented spherical Fourier-Bessel bases

https://github.com/zhaozh10/ChatCAD

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ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models

https://github.com/zhenxuan-zhang/tucl_brainseg

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Task-oriented Uncertainty Collaborative Learning for Label-Efficient Brain Tumor Segmentation

https://github.com/zhenzhao/ad-mt

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Alternate Diverse Teaching for Semi-supervised Medical Image Segmentation

https://github.com/zhenzhao/dpms

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Rethinking Data Perturbation and Model Stabilization for Semi-supervised Medical Image Segmentation

https://github.com/zhifangdeng/missformer

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MISSFormer: An Effective Medical Image Segmentation Transformer

https://github.com/zhilingyan/medical-sam-bench

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SAMed-2: Selective Memory Enhanced Medical Segment Anything Model

https://github.com/zhongaobo/asymmetry-bts

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Enhancing Incomplete Multi-modal Brain Tumor Segmentation with Intra-modal Asymmetry and Inter-modal Dependency

https://github.com/zhongxuanwang/adv_attk

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Susceptibility of Adversarial Attack on Medical Image Segmentation Models

https://github.com/zhuhui100/fasunet

공식 코드

FAS-UNet: A Novel FAS-driven Unet to Learn Variational Image Segmentation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.