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/fenghetan9/hi-end-mae

공식 코드

Hi-End-MAE: Hierarchical encoder-driven masked autoencoders are stronger vision learners for medical image segmentation

https://github.com/fenghetan9/llm4seg

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Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster

https://github.com/fenghetan9/mambamim

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MambaMIM: Pre-training Mamba with State Space Token Interpolation and its Application to Medical Image Segmentation

https://github.com/fenghetan9/mobileutr

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MobileUtr: Revisiting the relationship between light-weight CNN and Transformer for efficient medical image segmentation

https://github.com/fidshu/srsnet

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SRSNetwork: Siamese Reconstruction-Segmentation Networks based on Dynamic-Parameter Convolution

https://github.com/firstmover/cr-seg

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Consistency Regularization Improves Placenta Segmentation in Fetal EPI MRI Time Series

https://github.com/fitushar/Skin-lesion-Segmentation-using-grabcut

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Automatic Skin Lesion Segmentation Using GrabCut in HSV Colour Space

https://github.com/fjr9516/da_nnunet

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Unsupervised Domain Adaptation for Pediatric Brain Tumor Segmentation

https://github.com/fkhan98/medical-image-segmentation-with-attention-based-residual-double-u-net

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AttResDU-Net: Medical Image Segmentation Using Attention-based Residual Double U-Net

https://github.com/fmi-basel/RDCNet

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RDCNet: Instance segmentation with a minimalist recurrent residual network

https://github.com/frank-oy/hrefnet

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A Novel Hybrid Approach for Retinal Vessel Segmentation with Dynamic Long-Range Dependency and Multi-Scale Retinal Edge Fusion Enhancement

https://github.com/frankkramer-lab/MIScnn

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MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning

https://github.com/frankkramer-lab/miseval

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MISeval: a Metric Library for Medical Image Segmentation Evaluation

https://github.com/frankkramer-lab/miseval.analysis

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Towards a Guideline for Evaluation Metrics in Medical Image Segmentation

https://github.com/frankyu5666666/simpleunet

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Simple is what you need for efficient and accurate medical image segmentation

https://github.com/freesurfer/freesurfer/tree/dev/samseg

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An Open-Source Tool for Longitudinal Whole-Brain and White Matter Lesion Segmentation

https://github.com/freshman97/lintransunet

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Dynamic Linear Transformer for 3D Biomedical Image Segmentation

https://github.com/fsemerar/slicertomosam

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TomoSAM: a 3D Slicer extension using SAM for tomography segmentation

https://github.com/fudan-zvg/SETR

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Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

https://github.com/fxxjuses/micformer

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Multimodal Information Interaction for Medical Image Segmentation

https://github.com/gama-ufsc/brain-extraction-for-tumor-segmentation

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Towards fully automated deep-learning-based brain tumor segmentation: is brain extraction still necessary?

https://github.com/gaozhitong/SP_guided_Noisy_Label_Seg

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Superpixel-guided Iterative Learning from Noisy Labels for Medical Image Segmentation

https://github.com/gardnery/duedl

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DuEDL: Dual-Branch Evidential Deep Learning for Scribble-Supervised Medical Image Segmentation

https://github.com/ge-xing/hybridmim

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HybridMIM: A Hybrid Masked Image Modeling Framework for 3D Medical Image Segmentation

https://github.com/ge-xing/segmamba

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SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation

https://github.com/georgebatch/kvasir-seg

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Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges

https://github.com/gewelsji/errnet

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Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network

https://github.com/gewelsji/vps

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Video Polyp Segmentation: A Deep Learning Perspective

https://github.com/gift-surg/HighRes3DNet

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On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task

https://github.com/gift-surg/MONAIfbs

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MONAIfbs: MONAI-based fetal brain MRI deep learning segmentation

https://github.com/gigantenbein/UNet-Zoo

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RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification in Medical Image Segmentation

https://github.com/gillesvntnu/gcn_multistructure

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Towards Robust Cardiac Segmentation using Graph Convolutional Networks

https://github.com/gillesvntnu/gcn_unet_agreement_demo

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Towards Robust Cardiac Segmentation using Graph Convolutional Networks

https://github.com/gillesvntnu/graphbasedsegmentation

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Towards Robust Cardiac Segmentation using Graph Convolutional Networks

https://github.com/givkashi/focal-unet

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Focal-UNet: UNet-like Focal Modulation for Medical Image Segmentation

https://github.com/gmaresta/iW-Net

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iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network

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