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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/ai-med/halos

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HALOS: Hallucination-free Organ Segmentation after Organ Resection Surgery

https://github.com/ai-med/squeeze_and_excitation

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`Project & Excite' Modules for Segmentation of Volumetric Medical Scans

https://github.com/ai4colonoscopy/pranet-v2

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PraNet-V2: Dual-Supervised Reverse Attention for Medical Image Segmentation

https://github.com/aiconslab/roodmri

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ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRI

https://github.com/aidialab/retifluidnet

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RetiFluidNet: A Self-Adaptive and Multi-Attention Deep Convolutional Network for Retinal OCT Fluid Segmentation

https://github.com/aielte-research/MedSegPretrainImageNet

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Enhancing pretraining efficiency for medical image segmentation via transferability metrics

https://github.com/aieson/crossmatch

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CrossMatch: Enhance Semi-Supervised Medical Image Segmentation with Perturbation Strategies and Knowledge Distillation

https://github.com/aigeeksgroup/sss

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SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation

https://github.com/aijinrjinr/mlb-seg

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Consistency-guided Meta-Learning for Bootstrapping Semi-Supervised Medical Image Segmentation

https://github.com/aim-lab/PVBM

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PVBM: A Python Vasculature Biomarker Toolbox Based On Retinal Blood Vessel Segmentation

https://github.com/aim-skku/box-is

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IAM: Enhancing RGB-D Instance Segmentation with New Benchmarks

https://github.com/aim-skku/nyudv2-is

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IAM: Enhancing RGB-D Instance Segmentation with New Benchmarks

https://github.com/aim-skku/sun-rgbd-is

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IAM: Enhancing RGB-D Instance Segmentation with New Benchmarks

https://github.com/aim-uofa/genpercept

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What Matters When Repurposing Diffusion Models for General Dense Perception Tasks?

https://github.com/aim-uofa/sine

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A Simple Image Segmentation Framework via In-Context Examples

https://github.com/aimansnigdha/ambiguous-medical-image-segmentation-using-diffusion-models

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Ambiguous Medical Image Segmentation using Diffusion Models

https://github.com/aitechlabcn/neunet

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More complex encoder is not all you need

https://github.com/ajinkyakadu/DynamicXRayCT

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Single-shot Tomography of Discrete Dynamic Objects

https://github.com/ajoshiusc/brainseg

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Learning from imperfect training data using a robust loss function: application to brain image segmentation

https://github.com/akabircs/woundtissue

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Deep Learning for Wound Tissue Segmentation: A Comprehensive Evaluation using A Novel Dataset

https://github.com/akshay107/mimic-and-fool

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Mimic and Fool: A Task Agnostic Adversarial Attack

https://github.com/al3x-o-o-hung/csam

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CSAM: A 2.5D Cross-Slice Attention Module for Anisotropic Volumetric Medical Image Segmentation

https://github.com/alainjungo/reliability-challenges-uncertainty

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Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation

https://github.com/aleemsidra/SaLIP

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Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image Segmentation

https://github.com/alexanderhberger/topo-pitfalls

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Pitfalls of topology-aware image segmentation

https://github.com/alexdenker/eit_fenicsx

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Data-driven approaches for electrical impedance tomography image segmentation from partial boundary data

https://github.com/alexgraikos/diffusion_priors

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Diffusion models as plug-and-play priors

https://github.com/alexyouxin/explicit-shape-priors

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Learning with Explicit Shape Priors for Medical Image Segmentation

https://github.com/alfredqin/db-sam

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DB-SAM: Delving into High Quality Universal Medical Image Segmentation

https://github.com/alibaba-damo-academy/alice

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Anatomical Invariance Modeling and Semantic Alignment for Self-supervised Learning in 3D Medical Image Analysis

https://github.com/alibaba-damo-academy/ct-sam3d

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Towards a Comprehensive, Efficient and Promptable Anatomic Structure Segmentation Model using 3D Whole-body CT Scans

https://github.com/alibaba/uvosam

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UVOSAM: A Mask-free Paradigm for Unsupervised Video Object Segmentation via Segment Anything Model

https://github.com/alinlab/ifseg

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IFSeg: Image-free Semantic Segmentation via Vision-Language Model

https://github.com/alvinwen428/featurecp

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Predictive Inference with Feature Conformal Prediction

https://github.com/amazon-science/polygon-transformer

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PolyFormer: Referring Image Segmentation as Sequential Polygon Generation

https://github.com/amingolnari/Demo-Dynamic-Memory-Fusion-Framework

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Adaptive Real-Time Multi-Loss Function Optimization Using Dynamic Memory Fusion Framework: A Case Study on Breast Cancer Segmentation

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