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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/Guzaiwang/CE-Net

공식 코드

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

https://github.com/HUANGLIZI/ScribFormer

공식 코드

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

https://github.com/HaoZheng94/Ensemble

공식 코드

A New Ensemble Learning Framework for 3D Biomedical Image Segmentation

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

공식 코드

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

공식 코드

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

https://github.com/HelenMa9998/Selective_Uncertainty_AL

공식 코드

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

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

공식 코드

Learning Euler's Elastica Model for Medical Image Segmentation

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

공식 코드

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

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

공식 코드

Semi-supervised Medical Image Segmentation through Dual-task Consistency

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

공식 코드

Mutual Consistency Learning for Semi-supervised Medical Image Segmentation

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

공식 코드

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

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

공식 코드

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

https://github.com/HuXiaoling/TopoLoss

공식 코드

Topology-Preserving Deep Image Segmentation

https://github.com/HzFu/AGNet

공식 코드

Attention Guided Network for Retinal Image Segmentation

https://github.com/HzFu/COVID19_imaging_AI_paper_list

공식 코드

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

https://github.com/ISosnovik/nn4topopt

공식 코드

Neural networks for topology optimization

https://github.com/ISosnovik/top

공식 코드

Neural networks for topology optimization

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

공식 코드

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

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

공식 코드

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

https://github.com/JJGO/UniverSeg

공식 코드

UniverSeg: Universal Medical Image Segmentation

https://github.com/JeroenBertels/optimizingdice

공식 코드

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

https://github.com/JiYuanFeng/MCTrans

공식 코드

Multi-Compound Transformer for Accurate Biomedical Image Segmentation

https://github.com/Jianbo-Lab/LBIE

공식 코드

Language-Based Image Editing with Recurrent Attentive Models

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

공식 코드

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

https://github.com/JianqiangWan/Super-BPD

공식 코드

Super-BPD: Super Boundary-to-Pixel Direction for Fast Image Segmentation

https://github.com/JiaxiHe/Image-and-Video-Dehazing

공식 코드

Fast Single Image Dehazing via Multilevel Wavelet Transform based Optimization

https://github.com/JimTheBarbarian/Official-TTV-Segmentation

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An Image Segmentation Model with Transformed Total Variation

https://github.com/JingchunCheng/SegFlow

공식 코드

SegFlow: Joint Learning for Video Object Segmentation and Optical Flow

https://github.com/JoetheReindeer/multiPI_TransBTS

공식 코드

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

https://github.com/JoseLGomez/Co-training_SemSeg_UDA

공식 코드

Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models

https://github.com/JosephPB/XNet

공식 코드

XNet: A convolutional neural network (CNN) implementation for medical X-Ray image segmentation suitable for small datasets

https://github.com/Jun-Pu/UCOS-DA

공식 코드

Unsupervised Camouflaged Object Segmentation as Domain Adaptation

https://github.com/JunMa11/HM_DataAug

공식 코드

Histogram Matching Augmentation for Domain Adaptation with Application to Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Image Segmentation

https://github.com/JunMa11/SegLoss

공식 코드

Segmentation Loss Odyssey

https://github.com/JuzhengMiao/CPC-SAM

공식 코드

Cross Prompting Consistency with Segment Anything Model for Semi-supervised Medical Image Segmentation

https://github.com/KBergermann/MultilayerSSL

공식 코드

Semi-supervised Learning for Aggregated Multilayer Graphs Using Diffuse Interface Methods and Fast Matrix Vector Products

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