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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/huaibei-normal-university-cv-laboratory/mbdresunet

공식 코드

MBDRes-U-Net: Multi-Scale Lightweight Brain Tumor Segmentation Network

https://github.com/huanglizi/c2fvl

공식 코드

Coarse-to-Fine Covid-19 Segmentation via Vision-Language Alignment

https://github.com/huanglizi/lvit

공식 코드

LViT: Language meets Vision Transformer in Medical Image Segmentation

https://github.com/huanglizi/scribblevc

공식 코드

ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embedding

https://github.com/huanglizi/stpnet

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STPNet: Scale-aware Text Prompt Network for Medical Image Segmentation

https://github.com/huanglizi/tfcns

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TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation

https://github.com/huangmozhilv/u2net_torch

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3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation

https://github.com/hugo-oliveira/fsws_metalearning

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Meta-Learners for Few-Shot Weakly-Supervised Medical Image Segmentation

https://github.com/hui-po-wang/progfed

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ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training

https://github.com/huiimin5/aua

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Exploring Feature Representation Learning for Semi-supervised Medical Image Segmentation

https://github.com/huiimin5/comwin

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Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image Segmentation

https://github.com/hust-linyi/cat-net

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Few Shot Medical Image Segmentation with Cross Attention Transformer

https://github.com/hust-linyi/seg_trick

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Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directions

https://github.com/huster-hq/psdnet

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First-frame Supervised Video Polyp Segmentation via Propagative and Semantic Dual-teacher Network

https://github.com/hustvl/lkcell

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LKCell: Efficient Cell Nuclei Instance Segmentation with Large Convolution Kernels

https://github.com/hustxyy/fedia

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FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation Completeness

https://github.com/huyquoctrinh/PEFNet

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Multi Kernel Positional Embedding ConvNeXt for Polyp Segmentation

https://github.com/huyquoctrinh/kdas

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KDAS: Knowledge Distillation via Attention Supervision Framework for Polyp Segmentation

https://github.com/huyquoctrinh/kdas3

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KDAS: Knowledge Distillation via Attention Supervision Framework for Polyp Segmentation

https://github.com/huyquoctrinh/metapolyp-cbms2023

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Meta-Polyp: a baseline for efficient Polyp segmentation

https://github.com/hvcl/ScribbleCont

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Scribble-supervised Cell Segmentation Using Multiscale Contrastive Regularization

https://github.com/hvcl/scribble2label

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Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistency

https://github.com/hwei-hw/generalist_vision_foundation_models_for_medical_imaging

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Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation

https://github.com/hyunwoo137/metaseg

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MetaSeg: MetaFormer-based Global Contexts-aware Network for Efficient Semantic Segmentation

https://github.com/i-sahajmistry/nnunet_brats2023

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Multiscale Encoder and Omni-Dimensional Dynamic Convolution Enrichment in nnU-Net for Brain Tumor Segmentation

https://github.com/iMED-Lab/OCTA-Net-OCTA-Vessel-Segmentation-Network

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Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographs

https://github.com/iMED-Lab/WRB-Net

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Reconstruction and Quantification of 3D Iris Surface for Angle-Closure Glaucoma Detection in Anterior Segment OCT

https://github.com/iamjackyan/fed-lwr

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A New Perspective to Boost Performance Fairness for Medical Federated Learning

https://github.com/iantsen/hecktor

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Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images

https://github.com/ibm/multimodal-3d-image-segmentation

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FNOSeg3D: Resolution-Robust 3D Image Segmentation with Fourier Neural Operator

https://github.com/ictlab-unict/attention-cnn-MS-segmentation

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Boosting multiple sclerosis lesion segmentation through attention mechanism

https://github.com/igondia/matconvnet-dermoscopy

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Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for the Diagnosis of Skin Lesions

https://github.com/imatge-upc/liverseg-2017-nipsws

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Detection-aided liver lesion segmentation using deep learning

https://github.com/imatge-upc/medical-2017-nipsw

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Cost-Effective Active Learning for Melanoma Segmentation

https://github.com/imatge-upc/mri-braintumor-segmentation

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MRI brain tumor segmentation and uncertainty estimation using 3D-UNet architectures

https://github.com/imesluh/vocalfolds

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A Dataset of Laryngeal Endoscopic Images with Comparative Study on Convolution Neural Network Based Semantic Segmentation

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