Papers with Code 아카이브

Image Segmentation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

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/gvalvano/adversarial-test-time-training

공식 코드

Re-using Adversarial Mask Discriminators for Test-time Training under Distribution Shifts

https://github.com/gvalvano/multiscale-adversarial-attention-gates

공식 코드

Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates

https://github.com/gyglim/dvn

공식 코드

Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs

https://github.com/haail/glog-csunet

공식 코드

GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation

https://github.com/halleewong/ScribblePrompt

공식 코드

ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Image

https://github.com/hanbobizl/dmnet

공식 코드

Not Just Learning from Others but Relying on Yourself: A New Perspective on Few-Shot Segmentation in Remote Sensing

https://github.com/hanshounsu/d3rm

공식 코드

D3RM: A Discrete Denoising Diffusion Refinement Model for Piano Transcription

https://github.com/hanyue1648/reft

공식 코드

Reference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentation

https://github.com/haoli12345/cats

공식 코드

CATS v2: Hybrid encoders for robust medical segmentation

https://github.com/haoningwu3639/MRGen

공식 코드

MRGen: Diffusion-based Controllable Data Engine for MRI Segmentation towards Unannotated Modalities

https://github.com/haoshao-nku/medical_seg

공식 코드

MCANet: Medical Image Segmentation with Multi-Scale Cross-Axis Attention

https://github.com/haowang420/boemd-net

공식 코드

Inter-Rater Uncertainty Quantification in Medical Image Segmentation via Rater-Specific Bayesian Neural Networks

https://github.com/haranrk/DigiPathAI

공식 코드

A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and Analysis

https://github.com/hardyho/ultrametric_feature_fields

공식 코드

View-Consistent Hierarchical 3D Segmentation Using Ultrametric Feature Fields

https://github.com/harrytea/detect-anyshadow

공식 코드

Detect Any Shadow: Segment Anything for Video Shadow Detection

https://github.com/harvard-ophthalmology-ai-lab/fairdomain

공식 코드

FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification

https://github.com/harvard-ophthalmology-ai-lab/fairseg

공식 코드

FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scaling

https://github.com/hasibzunair/sharp-unets

공식 코드

Sharp U-Net: Depthwise Convolutional Network for Biomedical Image Segmentation

https://github.com/hbyecoding/u-rwkv

공식 코드

U-RWKV: Lightweight medical image segmentation with direction-adaptive RWKV

https://github.com/hci-unihd/pytorch-LearnedRandomWalker

공식 코드

End-to-End Learned Random Walker for Seeded Image Segmentation

https://github.com/healthml/active-segmentation

공식 코드

Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image Segmentation

https://github.com/healthx-lab/deepsegthoracic

공식 코드

Architecture Analysis and Benchmarking of 3D U-shaped Deep Learning Models for Thoracic Anatomical Segmentation

https://github.com/healthx-lab/medclip-samv2

공식 코드

MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentation

https://github.com/hejrati/cdal

공식 코드

Conditional diffusion model with spatial attention and latent embedding for medical image segmentation

https://github.com/helblazer811/ConceptAttention

공식 코드

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features

https://github.com/hendrycks/robustness

공식 코드

Quantifying the robustness of deep multispectral segmentation models against natural perturbations and data poisoning

https://github.com/heng14/gpu-net

공식 코드

GPU-Net: Lightweight U-Net with more diverse features

https://github.com/hengcai-nju/3d2dct

공식 코드

3D Medical Image Segmentation with Sparse Annotation via Cross-Teaching between 3D and 2D Networks

https://github.com/hengcai-nju/desco

공식 코드

Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentation

https://github.com/henry991115/mcfnet

공식 코드

Multi-scale Cascaded Large-Model for Whole-body ROI Segmentation

https://github.com/herschel555/caml

공식 코드

Correlation-Aware Mutual Learning for Semi-supervised Medical Image Segmentation

https://github.com/heshuting555/refmask3d

공식 코드

RefMask3D: Language-Guided Transformer for 3D Referring Segmentation

https://github.com/hfut-xc-yun/SpecTr

공식 코드

SpecTr: Spectral Transformer for Hyperspectral Pathology Image Segmentation

https://github.com/hhankyangg/sam-unet

공식 코드

SAM-UNet:Enhancing Zero-Shot Segmentation of SAM for Universal Medical Images

https://github.com/hic-messaoudi/cross-dimensional-transfer-learning-in-medical-image-segmentation-with-deep-learning

공식 코드

Cross-dimensional transfer learning in medical image segmentation with deep learning

https://github.com/hidneuron/3d-effivitcaps

공식 코드

3D-EffiViTCaps: 3D Efficient Vision Transformer with Capsule for Medical Image Segmentation

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