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/FightingFighting/GPS

공식 코드

Gradient-based Parameter Selection for Efficient Fine-Tuning

https://github.com/GewelsJI/FSNet

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Full-Duplex Strategy for Video Object Segmentation

https://github.com/GewelsJI/PNS-Net

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Video Polyp Segmentation: A Deep Learning Perspective

https://github.com/GinZhu/RDST

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A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuning

https://github.com/Guo-Xiaoqing/BraTS_OS

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Domain Knowledge Based Brain Tumor Segmentation and Overall Survival Prediction

https://github.com/Guo-Xiaoqing/Skin-Seg

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Complementary Network with Adaptive Receptive Fields for Melanoma Segmentation

https://github.com/Guzaiwang/CE-Net

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CE-Net: Context Encoder Network for 2D Medical Image Segmentation

https://github.com/HUANGLIZI/ScribFormer

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ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation

https://github.com/HaoZheng94/Ensemble

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A New Ensemble Learning Framework for 3D Biomedical Image Segmentation

https://github.com/HealthML/self-supervised-3d-tasks

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3D Self-Supervised Methods for Medical Imaging

https://github.com/HealthX-Lab/HGI-SAM

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Weakly Supervised Intracranial Hemorrhage Segmentation using Head-Wise Gradient-Infused Self-Attention Maps from a Swin Transformer in Categorical Learning

https://github.com/HealthX-Lab/MedCLIP-SAM

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MedCLIP-SAM: Bridging Text and Image Towards Universal Medical Image Segmentation

https://github.com/HelenMa9998/Selective_Uncertainty_AL

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Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image Segmentation

https://github.com/HiLab-git/ACELoss

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Learning Euler's Elastica Model for Medical Image Segmentation

https://github.com/HiLab-git/CA-Net

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CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation

https://github.com/HiLab-git/DTC

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Semi-supervised Medical Image Segmentation through Dual-task Consistency

https://github.com/HiLab-git/SSL4MIS

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Mutual Consistency Learning for Semi-supervised Medical Image Segmentation

https://github.com/HiLab-git/UGIR

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Uncertainty-Guided Efficient Interactive Refinement of Fetal Brain Segmentation from Stacks of MRI Slices

https://github.com/HiLab-git/WSL4MIS

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Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervision

https://github.com/Holmes696/Probabilistic-Hadamard-U-Net

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A Probabilistic Hadamard U-Net for MRI Bias Field Correction

https://github.com/HuCaoFighting/Swin-Unet

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Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation

https://github.com/HuaiChen-1994/LDLearning

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Unsupervised Local Discrimination for Medical Images

https://github.com/HzFu/COVID19_imaging_AI_paper_list

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Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation

https://github.com/IBBM/Cascaded-FCN

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Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields

https://github.com/Inha-CVAI/MADGNet

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Modality-agnostic Domain Generalizable Medical Image Segmentation by Multi-Frequency in Multi-Scale Attention

https://github.com/IoBT-VISTEC/OCTAve

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OCTAve: 2D en face Optical Coherence Tomography Angiography Vessel Segmentation in Weakly-Supervised Learning with Locality Augmentation

https://github.com/JJGO/UniverSeg

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UniverSeg: Universal Medical Image Segmentation

https://github.com/JLiangLab/SemanticGenesis

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Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration

https://github.com/JLiangLab/TransVW

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Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-supervised Learning

https://github.com/Jacobi93/Reward-penalty-Dice-loss

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Learning Non-Unique Segmentation with Reward-Penalty Dice Loss

https://github.com/JeroenBertels/optimizingdice

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Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice

https://github.com/JiajieMo/OctaveUNet

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Accurate Retinal Vessel Segmentation via Octave Convolution Neural Network

https://github.com/JianghaoWu/FPL-UDA

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CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation

https://github.com/Jiaxuan-Li/MGU-Net

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Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and disc in peripapillary OCT images

https://github.com/JieZheng-ShanghaiTech/CellVTA

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CellVTA: Enhancing Vision Foundation Models for Accurate Cell Segmentation and Classification

https://github.com/JoetheReindeer/multiPI_TransBTS

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multiPI-TransBTS: A Multi-Path Learning Framework for Brain Tumor Image Segmentation Based on Multi-Physical Information

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