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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/Murtadha44/-Intracranial-Hemorrhage-Segmentation-Using-Deep-Convolutional-Model-U-Net-

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Intracranial Hemorrhage Segmentation Using Deep Convolutional Model

https://github.com/NKI-AI/kandinsky-calibration

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Kandinsky Conformal Prediction: Efficient Calibration of Image Segmentation Algorithms

https://github.com/NUST-Machine-Intelligence-Laboratory/SMCP

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Semantically Meaningful Class Prototype Learning for One-Shot Image Semantic Segmentation

https://github.com/NathanUA/BASNet

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Boundary-Aware Segmentation Network for Mobile and Web Applications

https://github.com/Nexuslkl/Swin_MIL

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Transformer based multiple instance learning for weakly supervised histopathology image segmentation

https://github.com/Nisha1803/Methodology_Endosomal_Escape

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Stochastic Model of siRNA Endosomal Escape Mediated by Fusogenic Peptides in OVCAR-3

https://github.com/NormXU/Layout2Graph

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PARAGRAPH2GRAPH: A GNN-based framework for layout paragraph analysis

https://github.com/NoviceMAn-prog/MSRF-Net

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MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation

https://github.com/Omid-Nejati/Dilated_Unet

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Dilated-UNet: A Fast and Accurate Medical Image Segmentation Approach using a Dilated Transformer and U-Net Architecture

https://github.com/OpenGVLab/SAM-Med2D

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https://github.com/PJLallen/InstanceSAM2Eval

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Evaluation Study on SAM 2 for Class-agnostic Instance-level Segmentation

https://github.com/PKU-YuanGroup/LLMBind

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LLMBind: A Unified Modality-Task Integration Framework

https://github.com/PaddlePaddle/PaddleClas

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HS-ResNet: Hierarchical-Split Block on Convolutional Neural Network

https://github.com/PaddlePaddle/PaddleSeg

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PaddleSeg: A High-Efficient Development Toolkit for Image Segmentation

https://github.com/PengyiZhang/ACCL

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ACCL: Adversarial constrained-CNN loss for weakly supervised medical image segmentation

https://github.com/PerForm-Lab-RIT/domain-adaptation-eye-tracking

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Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking Systems

https://github.com/Powertony102/corn_official

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Leveraging CORAL-Correlation Consistency Network for Semi-Supervised Left Atrium MRI Segmentation

https://github.com/PramaLLC/BEN

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BEN: Using Confidence-Guided Matting for Dichotomous Image Segmentation

https://github.com/Prof-Pratik-Shah-Lab-UCI/skin_project_2024

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Uncertainty Quantified Deep Learning and Regression Analysis Framework for Image Segmentation of Skin Cancer Lesions

https://github.com/Project-MONAI/VISTA

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VISTA3D: Versatile Imaging SegmenTation and Annotation model for 3D Computed Tomography

https://github.com/Project-MONAI/research-contributions/tree/master/SwinUNETR/BRATS21

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Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

https://github.com/Project-MONAI/research-contributions/tree/master/UNETR/BTCV

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UNETR: Transformers for 3D Medical Image Segmentation

https://github.com/Project-MONAI/tutorials/tree/master/automl/DiNTS

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DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation

https://github.com/Purushothaman-natarajan/VALE-Explainer

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VALE: A Multimodal Visual and Language Explanation Framework for Image Classifiers using eXplainable AI and Language Models

https://github.com/PythonOT/POT/blob/master/ot/da.py

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Optimal Transport for Multi-source Domain Adaptation under Target Shift

https://github.com/QQTh4/NAS4Ultrasound

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SSHNN: Semi-Supervised Hybrid NAS Network for Echocardiographic Image Segmentation

https://github.com/QiZhangsama/P-MSDiff

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P-MSDiff: Parallel Multi-Scale Diffusion for Remote Sensing Image Segmentation

https://github.com/Qianyu1226/Crosslink-Net

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Crosslink-Net: Double-branch Encoder Segmentation Network via Fusing Vertical and Horizontal Convolutions

https://github.com/Qiming-Huang/ssformer

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Stepwise Feature Fusion: Local Guides Global

https://github.com/QingtaoPan/DuSSS

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DuSSS: Dual Semantic Similarity-Supervised Vision-Language Model for Semi-Supervised Medical Image Segmentation

https://github.com/RanSuLab/RAUNet-tumor-segmentation

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RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans

https://github.com/Rayicer/TransFuse

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TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation

https://github.com/RazvanDu/DUCK-Net

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Using DUCK-Net for Polyp Image Segmentation

https://github.com/Retinal-Research/EVIT-UNET

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EViT-Unet: U-Net Like Efficient Vision Transformer for Medical Image Segmentation on Mobile and Edge Devices

https://github.com/ReubenDo/InExtremIS

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Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation

https://github.com/RisabBiswas/Polyp-SAM-PlusPlus

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Polyp-SAM++: Can A Text Guided SAM Perform Better for Polyp Segmentation?

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