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/Qianyu1226/Crosslink-Net

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Crosslink-Net: Double-branch Encoder Segmentation Network via Fusing Vertical and Horizontal Convolutions

https://github.com/Qiming-Huang/ssformer

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Stepwise Feature Fusion: Local Guides Global

https://github.com/QingtaoPan/DuSSS

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DuSSS: Dual Semantic Similarity-Supervised Vision-Language Model for Semi-Supervised Medical Image Segmentation

https://github.com/Qsingle/MegVision

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SuperVessel: Segmenting High-resolution Vessel from Low-resolution Retinal Image

https://github.com/RL4M/PCRLv2

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PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis

https://github.com/RanSuLab/RAUNet-tumor-segmentation

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RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans

https://github.com/Rayicer/TransFuse

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TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation

https://github.com/RazvanDu/DUCK-Net

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Using DUCK-Net for Polyp Image Segmentation

https://github.com/Retinal-Research/EVIT-UNET

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EViT-Unet: U-Net Like Efficient Vision Transformer for Medical Image Segmentation on Mobile and Edge Devices

https://github.com/ReubenDo/jSTABL

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Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets

https://github.com/RisabBiswas/Polyp-SAM-PlusPlus

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Polyp-SAM++: Can A Text Guided SAM Perform Better for Polyp Segmentation?

https://github.com/RobinBruegger/PartiallyReversibleUnet

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A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation

https://github.com/RobinBruegger/RevTorch

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A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation

https://github.com/SAIL-GuoLab/Cell_Segmentation_and_Tracking

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Segmentation with Residual Attention U-Net and an Edge-Enhancement Approach Preserves Cell Shape Features

https://github.com/SHI-Labs/SGL-Retinal-Vessel-Segmentation

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Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels

https://github.com/SLDGroup/G-CASCADE

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G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation

https://github.com/SLDGroup/MERIT

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Multi-scale Hierarchical Vision Transformer with Cascaded Attention Decoding for Medical Image Segmentation

https://github.com/SMKamrulHasan/Learned-Condensation-Optimizaton-Network

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L-CO-Net: Learned Condensation-Optimization Network for Clinical Parameter Estimation from Cardiac Cine MRI

https://github.com/SZUHvern/D-UNet

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D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation

https://github.com/SarielMa/Increasing-Margin-Adversarial-Training

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Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural Networks

https://github.com/Scatteredrain/SALI

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SALI: Short-term Alignment and Long-term Interaction Network for Colonoscopy Video Polyp Segmentation

https://github.com/SharifAmit/RVGAN

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RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network

https://github.com/ShawkhIbneRashid/medical-images-sr

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Single MR Image Super-Resolution using Generative Adversarial Network

https://github.com/ShishuaiHu/BA-Net

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Boundary-Aware Network for Kidney Parsing

https://github.com/ShishuaiHu/DCAC

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Domain and Content Adaptive Convolution based Multi-Source Domain Generalization for Medical Image Segmentation

https://github.com/ShishuaiHu/Label-Propagation

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Label Propagation for 3D Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis

https://github.com/Shrajan/AAAI-2022

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3D-OOCS: Learning Prostate Segmentation with Inductive Bias

https://github.com/Shrajan/IB_U_Nets

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IB-U-Nets: Improving medical image segmentation tasks with 3D Inductive Biased kernels

https://github.com/Simael/mlds-unet

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Every Annotation Counts: Multi-label Deep Supervision for Medical Image Segmentation

https://github.com/SiyuLiu0329/python-3d-image-augmentation

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Generalisable 3D Fabric Architecture for Streamlined Universal Multi-Dataset Medical Image Segmentation

https://github.com/SiyuanYan1/PanDerm

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A Multimodal Vision Foundation Model for Clinical Dermatology

https://github.com/Stefan-Yu404/MP-UNet

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3D Medical Image Segmentation based on multi-scale MPU-Net

https://github.com/StefanDenn3r/Spatio-temporal-MS-Lesion-Segmentation

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Spatio-temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation

https://github.com/Suiiyu/MS-CMR2019

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An Automatic Cardiac Segmentation Framework based on Multi-sequence MR Image

https://github.com/TUM-AIMED/PrivateSegmentation

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Differentially private federated deep learning for multi-site medical image segmentation

https://github.com/TaehwanY98/Fed-Ref

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FedRef: Communication-Efficient Bayesian Fine Tuning with Reference Model

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