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/jeya-maria-jose/unext-pytorch

공식 코드

UNeXt: MLP-based Rapid Medical Image Segmentation Network

https://github.com/jhl-det/fla-net

공식 코드

Shifting More Attention to Breast Lesion Segmentation in Ultrasound Videos

https://github.com/jiahechen2002/dcf

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Decoupled Competitive Framework for Semi-supervised Medical Image Segmentation

https://github.com/jianf-wang/gbdl

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Rethinking Bayesian Deep Learning Methods for Semi-Supervised Volumetric Medical Image Segmentation

https://github.com/jianjianyin/dfen

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DFEN: Dual Feature Equalization Network for Medical Image Segmentation

https://github.com/jiarunliu/swin-umamba

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Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining

https://github.com/jiashu-xu/med-ttt

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Med-TTT: Vision Test-Time Training model for Medical Image Segmentation

https://github.com/jiayiliu-pku/ap2024

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Automated Lesion Segmentation in Whole-Body PET/CT in a multitracer setting

https://github.com/jiayiliu-pku/dc2024

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Enhancing Lesion Segmentation in PET/CT Imaging with Deep Learning and Advanced Data Preprocessing Techniques

https://github.com/jiazhang93/segmentation

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Whole-Body Lesion Segmentation in 18F-FDG PET/CT

https://github.com/jierunchen/tvconv

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TVConv: Efficient Translation Variant Convolution for Layout-aware Visual Processing

https://github.com/jinghangli98/wmh_seg

공식 코드

wmh_seg: Transformer based U-Net for Robust and Automatic White Matter Hyperintensity Segmentation across 1.5T, 3T and 7T

https://github.com/jinghuaman/aif-sfda

공식 코드

AIF-SFDA: Autonomous Information Filter-driven Source-Free Domain Adaptation for Medical Image Segmentation

https://github.com/jingnan-jia/segmentation_metrics

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Seg-metrics: a Python package to compute segmentation metrics

https://github.com/jingyechen/mt-transunet

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MT-TransUNet: Mediating Multi-Task Tokens in Transformers for Skin Lesion Segmentation and Classification

https://github.com/jiyuanfeng/amos2022

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AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation

https://github.com/jiyuuchc/lacss

공식 코드

Point-supervised Single-cell Segmentation via Collaborative Knowledge Sharing

https://github.com/jiyuuchc/lacss_jax

공식 코드

Point-supervised Single-cell Segmentation via Collaborative Knowledge Sharing

https://github.com/jizongFox/MI-based-Regularized-Semi-supervised-Segmentation

공식 코드

Boosting Semi-supervised Image Segmentation with Global and Local Mutual Information Regularization

https://github.com/jizongFox/Self-paced-Contrastive-Learning

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Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels

https://github.com/jmlipman/RatLesNetv2

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RatLesNetv2: A Fully Convolutional Network for Rodent Brain Lesion Segmentation

https://github.com/jmlipman/sauronunet

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Sauron U-Net: Simple automated redundancy elimination in medical image segmentation via filter pruning

https://github.com/jordan-colman/DR-Unet104

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DR-Unet104 for Multimodal MRI brain tumor segmentation

https://github.com/josedolz/3D-F-CNN-BrainStruct

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3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study

https://github.com/josedolz/HyperDenseNet

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HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation

https://github.com/josedolz/LiviaNET

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3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study

https://github.com/josedolz/MSL-student-becomes-master

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Segmentation with mixed supervision: Confidence maximization helps knowledge distillation

https://github.com/josedolz/SemiDenseNet

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Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation

https://github.com/joshualpf/lowbridge

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Bridging the Inter-Domain Gap through Low-Level Features for Cross-Modal Medical Image Segmentation

https://github.com/joyhsu0504/darcnn

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DARCNN: Domain Adaptive Region-based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical Images

https://github.com/juliawolleb/diffusion-based-segmentation

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Diffusion Models for Implicit Image Segmentation Ensembles

https://github.com/julien-zheng/CardiacMotionFlow

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Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow

https://github.com/julien-zheng/CardiacSegmentationPropagation

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3D Consistent & Robust Segmentation of Cardiac Images by Deep Learning with Spatial Propagation

https://github.com/jun-jie-shi/passion

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PASSION: Towards Effective Incomplete Multi-Modal Medical Image Segmentation with Imbalanced Missing Rates

https://github.com/junelin2333/languidemedseg-miccai2023

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Ariadne's Thread:Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray images

https://github.com/junqing-yang/mast

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MAST: Video Polyp Segmentation with a Mixture-Attention Siamese Transformer

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