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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/wrudman/NOTICE

공식 코드

What Do VLMs NOTICE? A Mechanistic Interpretability Pipeline for Gaussian-Noise-free Text-Image Corruption and Evaluation

https://github.com/wujiong-hub/cdpdnet

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CDPDNet: Integrating Text Guidance with Hybrid Vision Encoders for Medical Image Segmentation

https://github.com/wujunde/medical-sam-adapter

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Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

https://github.com/wujunde/medsegdiff

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MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model

https://github.com/wujunde/promptunet

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One-Prompt to Segment All Medical Images

https://github.com/wulalago/DAF3D

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Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound

https://github.com/wurenkai/MHA-UNet

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Only Positive Cases: 5-fold High-order Attention Interaction Model for Skin Segmentation Derived Classification

https://github.com/wurenkai/UltraLight-VM-UNet

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UltraLight VM-UNet: Parallel Vision Mamba Significantly Reduces Parameters for Skin Lesion Segmentation

https://github.com/wurenkai/h-vmunet

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H-vmunet: High-order Vision Mamba UNet for Medical Image Segmentation

https://github.com/wusize/clipself

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CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction

https://github.com/wutcm-lab/causalclipseg

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CausalCLIPSeg: Unlocking CLIP's Potential in Referring Medical Image Segmentation with Causal Intervention

https://github.com/wutcm-lab/shape-prior-semi-seg

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Striving for Simplicity: Simple Yet Effective Prior-Aware Pseudo-Labeling for Semi-Supervised Ultrasound Image Segmentation

https://github.com/wysnzzzz/dit

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Deep Instruction Tuning for Segment Anything Model

https://github.com/wysoczanska/clip-diy

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CLIP-DIY: CLIP Dense Inference Yields Open-Vocabulary Semantic Segmentation For-Free

https://github.com/wyxdm/amnet

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Focus on Query: Adversarial Mining Transformer for Few-Shot Segmentation

https://github.com/wzh0120/sam2-unet

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SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation

https://github.com/xamyzhao/brainstorm

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Data augmentation using learned transformations for one-shot medical image segmentation

https://github.com/xavysp/teed

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Tiny and Efficient Model for the Edge Detection Generalization

https://github.com/xhu248/autosam

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How to Efficiently Adapt Large Segmentation Model(SAM) to Medical Images

https://github.com/xhu248/cisfa_da

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Contrastive Image Synthesis and Self-supervised Feature Adaptation for Cross-Modality Biomedical Image Segmentation

https://github.com/xhu248/cond_ddpm_wsss

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Conditional Diffusion Models for Weakly Supervised Medical Image Segmentation

https://github.com/xhu248/semi_cotrast_seg

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Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation

https://github.com/xianlin7/apformer

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The Lighter The Better: Rethinking Transformers in Medical Image Segmentation Through Adaptive Pruning

https://github.com/xianlin7/batformer

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BATFormer: Towards Boundary-Aware Lightweight Transformer for Efficient Medical Image Segmentation

https://github.com/xianlin7/c2ftrans

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BATFormer: Towards Boundary-Aware Lightweight Transformer for Efficient Medical Image Segmentation

https://github.com/xianlin7/convformer

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ConvFormer: Plug-and-Play CNN-Style Transformers for Improving Medical Image Segmentation

https://github.com/xianlin7/samus

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Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Prompting

https://github.com/xiaofang007/cto

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Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation

https://github.com/xiaofang007/phnet

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Boosting Convolution with Efficient MLP-Permutation for Volumetric Medical Image Segmentation

https://github.com/xiaoqi-zhao-dlut/msnet

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M$^{2}$SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation

https://github.com/xiaoyao3302/2bdenoise

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Clean Label Disentangling for Medical Image Segmentation with Noisy Labels

https://github.com/xingqunqi-lab/gkd-framework

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Exploring Generalizable Distillation for Efficient Medical Image Segmentation

https://github.com/xinkunwang111/promise

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ProMISe: Promptable Medical Image Segmentation using SAM

https://github.com/xjiangmed/ltuda

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Labeled-to-Unlabeled Distribution Alignment for Partially-Supervised Multi-Organ Medical Image Segmentation

https://github.com/xmed-lab/GenericSSL

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Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation

https://github.com/xmed-lab/cpr

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Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image Segmentation

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