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Segmentation

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

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/hectorcarrion/fedd

공식 코드

FEDD -- Fair, Efficient, and Diverse Diffusion-based Lesion Segmentation and Malignancy Classification

https://github.com/hehao13/EBLNet

공식 코드

Enhanced Boundary Learning for Glass-like Object 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/helloworlder8/computer_vision

공식 코드

Iterative Optimization Annotation Pipeline and ALSS-YOLO-Seg for Efficient Banana Plantation Segmentation in UAV Imagery

https://github.com/hendrik-code/spineps

공식 코드

SPINEPS -- Automatic Whole Spine Segmentation of T2-weighted MR images using a Two-Phase Approach to Multi-class Semantic and Instance Segmentation

https://github.com/hendrycks/anomaly-seg

공식 코드

Scaling Out-of-Distribution Detection for Real-World Settings

https://github.com/heng-yuwen/matspectnet

공식 코드

MatSpectNet: Material Segmentation Network with Domain-Aware and Physically-Constrained Hyperspectral Reconstruction

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/henggao12/mmras_plus

공식 코드

Multi-modality Anomaly Segmentation on the Road

https://github.com/henghuiding/BFP

공식 코드

Boundary-Aware Feature Propagation for Scene Segmentation

https://github.com/henghuiding/MOSE-api

공식 코드

MOSE: A New Dataset for Video Object Segmentation in Complex Scenes

https://github.com/henghuiding/MeViS

공식 코드

MeViS: A Large-scale Benchmark for Video Segmentation with Motion Expressions

https://github.com/henghuiding/ReLA

공식 코드

GRES: Generalized Referring Expression Segmentation

https://github.com/henghuiding/SVC

공식 코드

Semantic Correlation Promoted Shape-Variant Context for Segmentation

https://github.com/henghuiding/Vision-Language-Transformer

공식 코드

Vision-Language Transformer and Query Generation for Referring Segmentation

https://github.com/hengliusky/few_shot_rvos

공식 코드

Few-Shot Referring Video Single- and Multi-Object Segmentation via Cross-Modal Affinity with Instance Sequence Matching

https://github.com/henry991115/mcfnet

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

https://github.com/hermannsblum/fishyscapes

공식 코드

The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation

https://github.com/herschel555/caml

공식 코드

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

https://github.com/heshuting555/D2Zero

공식 코드

Semantic-Promoted Debiasing and Background Disambiguation for Zero-Shot Instance Segmentation

https://github.com/heshuting555/PADing

공식 코드

Primitive Generation and Semantic-related Alignment for Universal Zero-Shot Segmentation

https://github.com/heshuting555/pap-fzs3d

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Prototype Adaption and Projection for Few- and Zero-shot 3D Point Cloud Semantic Segmentation

https://github.com/heshuting555/refmask3d

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RefMask3D: Language-Guided Transformer for 3D Referring Segmentation

https://github.com/hfslyc/AdvSemiSeg

공식 코드

Adversarial Learning for Semi-Supervised Semantic Segmentation

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

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SpecTr: Spectral Transformer for Hyperspectral Pathology Image Segmentation

https://github.com/hhaentze/mrsegmentator

공식 코드

MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT

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

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

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

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

https://github.com/hiest/distmis

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Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation

https://github.com/hieuphamha19/csds

공식 코드

Learning Disentangled Stain and Structural Representations for Semi-Supervised Histopathology Segmentation

https://github.com/hilab-git/dag4mia

공식 코드

CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image Segmentation

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