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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/siyi-wind/mdvit

공식 코드

MDViT: Multi-domain Vision Transformer for Small Medical Image Segmentation Datasets

https://github.com/sky-visionx/cracksegdiff

공식 코드

CrackSegDiff: Diffusion Probability Model-based Multi-modal Crack Segmentation

https://github.com/slamcore/semlaps

공식 코드

SeMLaPS: Real-time Semantic Mapping with Latent Prior Networks and Quasi-Planar Segmentation

https://github.com/sldgroup/emcad

공식 코드

EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation

https://github.com/smallmax00/bi-gconv

공식 코드

BI-GCN: Boundary-Aware Input-Dependent Graph Convolution Network for Biomedical Image Segmentation

https://github.com/smkamrulhasan/mt-ctl

공식 코드

A Multi-Task Cross-Task Learning Architecture for Ad-hoc Uncertainty Estimation in 3D Cardiac MRI Image Segmentation

https://github.com/smkamrulhasan/mtctl

공식 코드

A Multi-Task Cross-Task Learning Architecture for Ad-hoc Uncertainty Estimation in 3D Cardiac MRI Image Segmentation

https://github.com/snavalm/mood22

공식 코드

Achieving state-of-the-art performance in the Medical Out-of-Distribution (MOOD) challenge using plausible synthetic anomalies

https://github.com/snnresearch/spiking-unet

공식 코드

Deep Multi-Threshold Spiking-UNet for Image Processing

https://github.com/songchangwei/3dct-sd-ivh-ich

공식 코드

HemSeg-200: A Voxel-Annotated Dataset for Intracerebral Hemorrhages Segmentation in Brain CT Scans

https://github.com/songw-zju/Meta-RangeSeg

공식 코드

Meta-RangeSeg: LiDAR Sequence Semantic Segmentation Using Multiple Feature Aggregation

https://github.com/soolab/simcis

공식 코드

Rethinking Query-based Transformer for Continual Image Segmentation

https://github.com/sophieostmeier/uncertainsmallempty

공식 코드

USE-Evaluator: Performance Metrics for Medical Image Segmentation Models with Uncertain, Small or Empty Reference Annotations

https://github.com/sotiraslab/AgileFormer

공식 코드

AgileFormer: Spatially Agile Transformer UNet for Medical Image Segmentation

https://github.com/sotiraslab/SC-VAE

공식 코드

SC-VAE: Sparse Coding-based Variational Autoencoder with Learned ISTA

https://github.com/sotiraslab/dlk

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D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image Segmentation

https://github.com/soumbane/dualselfdistillation

공식 코드

Volumetric medical image segmentation through dual self-distillation in U-shaped networks

https://github.com/soumickmj/pulaski

공식 코드

PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation

https://github.com/spamlab/data_sharing

공식 코드

Relict landslide detection using Deep-Learning architectures for image segmentation in rainforest areas: A new framework

https://github.com/speechbrain/speechbrain

공식 코드

Posthoc Interpretation via Quantization

https://github.com/spidernitt/sam-pm

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SAM-PM: Enhancing Video Camouflaged Object Detection using Spatio-Temporal Attention

https://github.com/splinterli/sattca

공식 코드

Scale-aware Test-time Click Adaptation for Pulmonary Nodule and Mass Segmentation

https://github.com/spyflying/CMPC-Refseg

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Referring Image Segmentation via Cross-Modal Progressive Comprehension

https://github.com/spyflying/LSCM-Refseg

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Linguistic Structure Guided Context Modeling for Referring Image Segmentation

https://github.com/sqbqamar/SAMA-UNet

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SAMA-UNet: Enhancing Medical Image Segmentation with Self-Adaptive Mamba-Like Attention and Causal-Resonance Learning

https://github.com/sr0920/cit-net

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CiT-Net: Convolutional Neural Networks Hand in Hand with Vision Transformers for Medical Image Segmentation

https://github.com/sr0920/tec-net

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TEC-Net: Vision Transformer Embrace Convolutional Neural Networks for Medical Image Segmentation

https://github.com/ssen7/histo_cap_generation_2

공식 코드

Automatic Report Generation for Histopathology images using pre-trained Vision Transformers

https://github.com/ssen7/histo_cap_transformers

공식 코드

Automatic Report Generation for Histopathology images using pre-trained Vision Transformers and BERT

https://github.com/ssyc123/siseg

공식 코드

Synthetic Instance Segmentation from Semantic Image Segmentation Masks

https://github.com/ssyc123/tfiseg

공식 코드

Synthetic Instance Segmentation from Semantic Image Segmentation Masks

https://github.com/stanfordmlgroup/CheXseg

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CheXseg: Combining Expert Annotations with DNN-generated Saliency Maps for X-ray Segmentation

https://github.com/stanfordmlgroup/auto-generate-wls

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Auto-Generating Weak Labels for Real & Synthetic Data to Improve Label-Scarce Medical Image Segmentation

https://github.com/stanley-313/imagesegfm-survey

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Image Segmentation in Foundation Model Era: A Survey

https://github.com/starbucks-drinker/mi-segnet

공식 코드

MI-SegNet: Mutual Information-Based US Segmentation for Unseen Domain Generalization

https://github.com/statmlben/rankseg

공식 코드

RankSEG: A Consistent Ranking-based Framework for Segmentation

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