Papers with Code 아카이브

Image Segmentation

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하위 태스크: 1
평가표: 13
보존된 평가 기록: 34

Image Segmentation is a computer vision task that involves dividing an image into multiple segments or regions, each of which corresponds to a different object or part of an object. The goal of image segmentation is to assign a unique label or category to each pixel in the image, so that pixels with similar attributes are grouped together.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/xmed-lab/dhc

공식 코드

DHC: Dual-debiased Heterogeneous Co-training Framework for Class-imbalanced Semi-supervised Medical Image Segmentation

https://github.com/xmed-lab/epl_semidg

공식 코드

Enhancing Pseudo Label Quality for Semi-Supervised Domain-Generalized Medical Image Segmentation

https://github.com/xmeimeimei/sgtc

공식 코드

SGTC: Semantic-Guided Triplet Co-training for Sparsely Annotated Semi-Supervised Medical Image Segmentation

https://github.com/xmindflow/cenet

공식 코드

CENet: Context Enhancement Network for Medical Image Segmentation

https://github.com/xmindflow/deformableLKA

공식 코드

Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation

https://github.com/xmindflow/fusenet

공식 코드

FuseNet: Self-Supervised Dual-Path Network for Medical Image Segmentation

https://github.com/xmindflow/lhunet

공식 코드

LHU-Net: A Light Hybrid U-Net for Cost-Efficient, High-Performance Volumetric Medical Image Segmentation

https://github.com/xmindflow/msa-2net

공식 코드

MSA$^2$Net: Multi-scale Adaptive Attention-guided Network for Medical Image Segmentation

https://github.com/xq141839/DCSAU-Net

공식 코드

DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation

https://github.com/xq141839/HRMedSeg

공식 코드

HRMedSeg: Unlocking High-resolution Medical Image segmentation via Memory-efficient Attention Modeling

https://github.com/xq141839/esp-medsam

공식 코드

ESP-MedSAM: Efficient Self-Prompting SAM for Universal Domain-Generalized Medical Image Segmentation

https://github.com/xq141839/nusegdg

공식 코드

NuSegDG: Integration of Heterogeneous Space and Gaussian Kernel for Domain-Generalized Nuclei Segmentation

https://github.com/xq141839/sppnet

공식 코드

SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation

https://github.com/xuanrui-work/wsssbyrec

공식 코드

Semantic Segmentation from Image Labels by Reconstruction from Structured Decomposition

https://github.com/xuebinqin/DIS

공식 코드

Highly Accurate Dichotomous Image Segmentation

https://github.com/xuzhang1211/pvpuformer

공식 코드

PVPUFormer: Probabilistic Visual Prompt Unified Transformer for Interactive Image Segmentation

https://github.com/xuzhang1211/vpuformer

공식 코드

PVPUFormer: Probabilistic Visual Prompt Unified Transformer for Interactive Image Segmentation

https://github.com/xuzhez/mapseg

공식 코드

MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling

https://github.com/xwmaxwma/rssegmentation

공식 코드

LOGCAN++: Adaptive Local-global class-aware network for semantic segmentation of remote sensing imagery

https://github.com/xxxliu95/RA_FA_Cardiac

공식 코드

Disentangled Representations for Domain-generalized Cardiac Segmentation

https://github.com/xychen2022/VersatileSegmentation

공식 코드

Versatile Medical Image Segmentation Learned from Multi-Source Datasets via Model Self-Disambiguation

https://github.com/xyimaging/fnpc

공식 코드

False Negative/Positive Control for SAM on Noisy Medical Images

https://github.com/xyx1024/SimTxtSeg

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SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text Cues

https://github.com/xzz2/pa-sam

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PA-SAM: Prompt Adapter SAM for High-Quality Image Segmentation

https://github.com/y-tarl/c3s3

공식 코드

C3S3: Complementary Competition and Contrastive Selection for Semi-Supervised Medical Image Segmentation

https://github.com/yangfei1223/crfconv

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Continuous Conditional Random Field Convolution for Point Cloud Segmentation

https://github.com/yanglanou/patcher

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Patcher: Patch Transformers with Mixture of Experts for Precise Medical Image Segmentation

https://github.com/yangyang-69/Prompt_sam_cryoPPP

공식 코드

Adapting Segment Anything Model (SAM) through Prompt-based Learning for Enhanced Protein Identification in Cryo-EM Micrographs

https://github.com/yangzhangcst/afa-graph

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Adaptive Fusion Affinity Graph with Noise-free Online Low-rank Representation for Natural Image Segmentation

https://github.com/yaoppeng/u-net_v2

공식 코드

U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation

https://github.com/yaoruluo/meta-structures-for-dnn

공식 코드

Elucidating Meta-Structures of Noisy Labels in Semantic Segmentation by Deep Neural Networks

https://github.com/yayafengzi/LMM-HiMTok

공식 코드

HiMTok: Learning Hierarchical Mask Tokens for Image Segmentation with Large Multimodal Model

https://github.com/yazhouzhu19/rpt

공식 코드

Few-Shot Medical Image Segmentation via a Region-enhanced Prototypical Transformer

https://github.com/yaziciz/GLIMS

공식 코드

GLIMS: Attention-Guided Lightweight Multi-Scale Hybrid Network for Volumetric Semantic Segmentation

https://github.com/ycwu1997/d-persona

공식 코드

Diversified and Personalized Multi-rater Medical Image Segmentation

https://github.com/ycwu1997/mc-net

공식 코드

Mutual Consistency Learning for Semi-supervised Medical Image Segmentation

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