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/gndlwch2w/msvm-unet

공식 코드

MSVM-UNet: Multi-Scale Vision Mamba UNet for Medical Image Segmentation

https://github.com/goblin327/segkan

공식 코드

SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies

https://github.com/goblin327/segstitch

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SegStitch: Multidimensional Transformer for Robust and Efficient Medical Imaging Segmentation

https://github.com/gorkemcanates/dual-cross-attention

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Dual Cross-Attention for Medical Image Segmentation

https://github.com/guanghaozhu663/astmatch

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AstMatch: Adversarial Self-training Consistency Framework for Semi-Supervised Medical Image Segmentation

https://github.com/gugull123/stac

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Shape Transformation Driven by Active Contour for Class-Imbalanced Semi-Supervised Medical Image Segmentation

https://github.com/guomugong/BTS-DSN

공식 코드

BTS-DSN: Deeply Supervised Neural Network with Short Connections for Retinal Vessel Segmentation

https://github.com/gvalvano/adversarial-test-time-training

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Re-using Adversarial Mask Discriminators for Test-time Training under Distribution Shifts

https://github.com/gvalvano/multiscale-adversarial-attention-gates

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Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates

https://github.com/gykovacs/retina_vessel_segmentation

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A new baseline for retinal vessel segmentation: Numerical identification and correction of methodological inconsistencies affecting 100+ papers

https://github.com/haail/fins-for-biomedical-imaging

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Feature Imitating Networks Enhance The Performance, Reliability And Speed Of Deep Learning On Biomedical Image Processing Tasks

https://github.com/haail/glog-csunet

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GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation

https://github.com/hakal104/autopetiii

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Autopet III challenge: Incorporating anatomical knowledge into nnUNet for lesion segmentation in PET/CT

https://github.com/halleewong/ScribblePrompt

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ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Image

https://github.com/hamghalam/MGP-VAE

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Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma Segmentation

https://github.com/han-liu/moddropplusplus

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ModDrop++: A Dynamic Filter Network with Intra-subject Co-training for Multiple Sclerosis Lesion Segmentation with Missing Modalities

https://github.com/hanktseng131415go/ramem

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Real-time Automatic M-mode Echocardiography Measurement with Panel Attention from Local-to-Global Pixels

https://github.com/haoli12345/cats

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CATS v2: Hybrid encoders for robust medical segmentation

https://github.com/haoningwu3639/MRGen

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MRGen: Diffusion-based Controllable Data Engine for MRI Segmentation towards Unannotated Modalities

https://github.com/haoshao-nku/medical_seg

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MCANet: Medical Image Segmentation with Multi-Scale Cross-Axis Attention

https://github.com/haowang420/boemd-net

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Inter-Rater Uncertainty Quantification in Medical Image Segmentation via Rater-Specific Bayesian Neural Networks

https://github.com/haoxuanli-thu/hcma-unet

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HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation

https://github.com/harvard-ophthalmology-ai-lab/fairdomain

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FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification

https://github.com/harvard-ophthalmology-ai-lab/fairseg

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FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scaling

https://github.com/harveerar/PSIGAN

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PSIGAN: Joint probabilistic segmentation and image distribution matching for unpaired cross-modality adaptation based MRI segmentation

https://github.com/hbyecoding/u-rwkv

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U-RWKV: Lightweight medical image segmentation with direction-adaptive RWKV

https://github.com/healthml/active-segmentation

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Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image Segmentation

https://github.com/healthx-lab/deepsegthoracic

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Architecture Analysis and Benchmarking of 3D U-shaped Deep Learning Models for Thoracic Anatomical Segmentation

https://github.com/healthx-lab/medclip-samv2

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MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentation

https://github.com/hectorcarrion/fedd

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FEDD -- Fair, Efficient, and Diverse Diffusion-based Lesion Segmentation and Malignancy Classification

https://github.com/hejrati/cdal

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Conditional diffusion model with spatial attention and latent embedding for medical image segmentation

https://github.com/hengcai-nju/3d2dct

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3D Medical Image Segmentation with Sparse Annotation via Cross-Teaching between 3D and 2D Networks

https://github.com/hengcai-nju/desco

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Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentation

https://github.com/herschel555/caml

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Correlation-Aware Mutual Learning for Semi-supervised Medical Image Segmentation

https://github.com/hhankyangg/sam-unet

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SAM-UNet:Enhancing Zero-Shot Segmentation of SAM for Universal Medical Images

https://github.com/hic-messaoudi/cross-dimensional-transfer-learning-in-medical-image-segmentation-with-deep-learning

공식 코드

Cross-dimensional transfer learning in medical image segmentation with deep learning

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