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/oulu-imeds/sauna

공식 코드

Image-level Regression for Uncertainty-aware Retinal Image Segmentation

https://github.com/oulu-imeds/singr

공식 코드

SiNGR: Brain Tumor Segmentation via Signed Normalized Geodesic Transform Regression

https://github.com/ozan-oktay/Attention-Gated-Networks

공식 코드

Attention U-Net: Learning Where to Look for the Pancreas

https://github.com/pablohc97/TFM

공식 코드

Neural Networks with Divisive normalization for image segmentation with application in cityscapes dataset

https://github.com/pascalcpp/sdcl

공식 코드

SDCL: Students Discrepancy-Informed Correction Learning for Semi-supervised Medical Image Segmentation

https://github.com/pashtari/deconver

공식 코드

Deconver: A Deconvolutional Network for Medical Image Segmentation

https://github.com/pashtari/factorizer

공식 코드

Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image Segmentation

https://github.com/pathak22/ccnn

공식 코드

Constrained Convolutional Neural Networks for Weakly Supervised Segmentation

https://github.com/patrick-knab/dseg-lime

공식 코드

Beyond Pixels: Enhancing LIME with Hierarchical Features and Segmentation Foundation Models

https://github.com/paulcouairon/diffcut

공식 코드

DiffCut: Catalyzing Zero-Shot Semantic Segmentation with Diffusion Features and Recursive Normalized Cut

https://github.com/pbizopoulos/comprehensive-comparison-of-deep-learning-models-for-lung-and-covid-19-lesion-segmentation-in-ct

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Comprehensive Comparison of Deep Learning Models for Lung and COVID-19 Lesion Segmentation in CT scans

https://github.com/penaud-polge/fully-trainable-gaussian-derivative-layer

공식 코드

Fully trainable Gaussian derivative convolutional layer

https://github.com/pengchengshi1220/nextou

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NexToU: Efficient Topology-Aware U-Net for Medical Image Segmentation

https://github.com/pengchihan/binaryimagedownsampling

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Topology-Preserving Downsampling of Binary Images

https://github.com/perceptioncomputinglab/instance2022

공식 코드

The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge

https://github.com/perslev/MultiPlanarUNet

공식 코드

One Network to Segment Them All: A General, Lightweight System for Accurate 3D Medical Image Segmentation

https://github.com/peteryyzhang/few-shot-self-prompt-sam

공식 코드

Self-Prompting Large Vision Models for Few-Shot Medical Image Segmentation

https://github.com/petteriTeikari/vesselNN

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Deep Learning Convolutional Networks for Multiphoton Microscopy Vasculature Segmentation

https://github.com/petteriTeikari/vesselNN_dataset

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Deep Learning Convolutional Networks for Multiphoton Microscopy Vasculature Segmentation

https://github.com/phonchi/cryoparticlesegment

공식 코드

CRISP: A Framework for Cryo-EM Image Segmentation and Processing with Conditional Random Field

https://github.com/phy710/icassp2024-fdg-st

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Domain Generalization with Fourier Transform and Soft Thresholding

https://github.com/physiologicailab/sam-cl

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Self-adversarial Multi-scale Contrastive Learning for Semantic Segmentation of Thermal Facial Images

https://github.com/piooip/ud-mamba

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UD-Mamba: A pixel-level uncertainty-driven Mamba model for medical image segmentation

https://github.com/pjlallen/udun

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Unite-Divide-Unite: Joint Boosting Trunk and Structure for High-accuracy Dichotomous Image Segmentation

https://github.com/pjsjongsung/PUMBA

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Skull stripping with purely synthetic data

https://github.com/pjsjongsung/evac

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EVAC+: Multi-scale V-net with Deep Feature CRF Layers for Brain Extraction

https://github.com/pldelisle/deepNormalize

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Realistic Image Normalization for Multi-Domain Segmentation

https://github.com/pnnl/SAMIAm

공식 코드

SAM-I-Am: Semantic Boosting for Zero-shot Atomic-Scale Electron Micrograph Segmentation

https://github.com/pnnl/pychip_gui

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Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy Data

https://github.com/pranavsinghps1/s4mi

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Shifting to Machine Supervision: Annotation-Efficient Semi and Self-Supervised Learning for Automatic Medical Image Segmentation and Classification

https://github.com/prerakmody/bayesuncertainty-error-correspondence

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Improving Uncertainty-Error Correspondence in Deep Bayesian Medical Image Segmentation

https://github.com/primebo1/FAMNet

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FAMNet: Frequency-aware Matching Network for Cross-domain Few-shot Medical Image Segmentation

https://github.com/prlab21/maxvit-unet

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MaxViT-UNet: Multi-Axis Attention for Medical Image Segmentation

https://github.com/programmerhyy/cm-mlp

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CM-MLP: Cascade Multi-scale MLP with Axial Context Relation Encoder for Edge Segmentation of Medical Image

https://github.com/project-monai/research-contributions

공식 코드

UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image Segmentation

https://github.com/prowontheus/isd-mae

공식 코드

Intensity-Spatial Dual Masked Autoencoder for Multi-Scale Feature Learning in Chest CT Segmentation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.