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/nki-radiology/interpretability_augmentation

공식 코드

Interpretability-guided Data Augmentation for Robust Segmentation in Multi-centre Colonoscopy Data

https://github.com/nkicsl/sf-unet

공식 코드

Spatial-Frequency Dual Progressive Attention Network For Medical Image Segmentation

https://github.com/nobodyplayer1/vm-unetv2

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VM-UNET-V2 Rethinking Vision Mamba UNet for Medical Image Segmentation

https://github.com/notmatthancock/level-set-machine-learning

공식 코드

Level set image segmentation with velocity term learned from data with applications to lung nodule segmentation

https://github.com/noureddinekhiati/semi-supervised-lung-segmentation

공식 코드

Shape-aware synthesis of pathological lung CT scans using CycleGAN for enhanced semi-supervised lung segmentation

https://github.com/novicefodder/os-medseg

공식 코드

Distillation Learning Guided by Image Reconstruction for One-Shot Medical Image Segmentation

https://github.com/nspunn1993/bt-unet

공식 코드

BT-Unet: A self-supervised learning framework for biomedical image segmentation using Barlow Twins with U-Net models

https://github.com/nstucki/betti-matching

공식 코드

Topologically faithful image segmentation via induced matching of persistence barcodes

https://github.com/ntnu-smallsat-lab/s_l_c_segm_hyp_img

공식 코드

Semantic Segmentation in Satellite Hyperspectral Imagery by Deep Learning

https://github.com/nubagcilab/diffboost

공식 코드

DiffBoost: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model

https://github.com/numisveinsson/SeqSeg

공식 코드

SeqSeg: Learning Local Segments for Automatic Vascular Model Construction

https://github.com/nus-tim/dems

공식 코드

Segmenting Medical Images with Limited Data

https://github.com/nv-tlabs/datasetGAN_release

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DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort

https://github.com/nvakhoa/superclass-fsis

공식 코드

Instance-level Few-shot Learning with Class Hierarchy Mining

https://github.com/nveldt/SparseCardDSFM

공식 코드

Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components

https://github.com/nvidia/torch-harmonics

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Attention on the Sphere

https://github.com/oesteban/MBIS

공식 코드

MBIS: Multivariate Bayesian Image Segmentation Tool

https://github.com/oliverjm1/bmvc_menisc_seg

공식 코드

Putting the Segment Anything Model to the Test with 3D Knee MRI - A Comparison with State-of-the-Art Performance

https://github.com/oliverrensu/mvg

공식 코드

Medical Vision Generalist: Unifying Medical Imaging Tasks in Context

https://github.com/olivesgatech/pointprompt

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Benchmarking Human and Automated Prompting in the Segment Anything Model

https://github.com/omid-nejati/befunet

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BEFUnet: A Hybrid CNN-Transformer Architecture for Precise Medical Image Segmentation

https://github.com/onvungocminh/pathloss

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BuyTheDips: PathLoss for improved topology-preserving deep learning-based image segmentation

https://github.com/open-forest-observatory/tree-detection-framework

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Zero-Shot Tree Detection and Segmentation from Aerial Forest Imagery

https://github.com/opengeos/segment-geospatial

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The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot

https://github.com/openmedlab/medlsam

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MedLSAM: Localize and Segment Anything Model for 3D CT Images

https://github.com/openmedlab/mis-fm

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MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset

https://github.com/opradelle/2dguidedlight3d

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Lightweight integration of 3D features to improve 2D image segmentation

https://github.com/orange-czh/mamba-sea

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Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation

https://github.com/ortonwang/pcdal

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PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification

https://github.com/ortonwang/plgdf

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Pseudo Label-Guided Data Fusion and Output Consistency for Semi-Supervised Medical Image Segmentation

https://github.com/ortonwang/scribblevs

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ScribbleVS: Scribble-Supervised Medical Image Segmentation via Dynamic Competitive Pseudo Label Selection

https://github.com/osupcvlab/mobileunetr

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MobileUNETR: A Lightweight End-To-End Hybrid Vision Transformer For Efficient Medical Image Segmentation

https://github.com/osupcvlab/segformer3d

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SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation

https://github.com/othmanela/certified_segmentation

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Towards Better Certified Segmentation via Diffusion Models

https://github.com/othmanela/medical_cert_seg

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Certification of Deep Learning Models for Medical Image Segmentation

https://github.com/oulu-imeds/log-vmamba

공식 코드

LoG-VMamba: Local-Global Vision Mamba for Medical Image Segmentation

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