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Medical Image Segmentation

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하위 태스크: 31
평가표: 49
보존된 평가 기록: 332

Medical Image Segmentation is a computer vision task that involves dividing an medical image into multiple segments, where each segment represents a different object or structure of interest in the image. The goal of medical image segmentation is to provide a precise and accurate representation of the objects of interest within the image, typically for the purpose of diagnosis, treatment planning, and quantitative analysis.

<span style="color:grey; opacity: 0.6">( Image credit: IVD-Net )</span>

코드

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

https://github.com/weathon/vcos

공식 코드

ZS-VCOS: Zero-Shot Outperforms Supervised Video Camouflaged Object Segmentation

https://github.com/weijun88/weakpolyp

공식 코드

WeakPolyp: You Only Look Bounding Box for Polyp Segmentation

https://github.com/wendywaaaaang/a4-unet

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A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation

https://github.com/wenhui0206/MeanTeacher-DeepMedic

공식 코드

Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model

https://github.com/wenruxue/detta

공식 코드

From Denoising Training to Test-Time Adaptation: Enhancing Domain Generalization for Medical Image Segmentation

https://github.com/wetothemoon/braintumorsegmentation

공식 코드

Brain Tumor Segmentation Based on Deep Learning, Attention Mechanisms, and Energy-Based Uncertainty Prediction

https://github.com/whdong-whu/sikd

공식 코드

Shape-intensity knowledge distillation for robust medical image segmentation

https://github.com/white65534/bhsd

공식 코드

BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Dataset

https://github.com/willbeanyang/swin-der

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Optimizing Medical Image Segmentation with Advanced Decoder Design

https://github.com/wimmerth/scale-equivariant-3d-convnet

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Scale-Equivariant Deep Learning for 3D Data

https://github.com/windstormer/Cfd-CAM

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A Novel Confidence Induced Class Activation Mapping for MRI Brain Tumor Segmentation

https://github.com/windstormer/ame-cam

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AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain Tumor

https://github.com/windygoo/promptnucseg

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Unleashing the Power of Prompt-driven Nucleus Instance Segmentation

https://github.com/wjh892521292/lkm-unet

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LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image Segmentation

https://github.com/wjh892521292/lma-unet

공식 코드

LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image Segmentation

https://github.com/wkklavis/dapsam

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Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation

https://github.com/wnn2000/fedaaai

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FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise

https://github.com/woodywff/brats_2019

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3D U-Net Based Brain Tumor Segmentation and Survival Days Prediction

https://github.com/woodywff/nas_3d_unet

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Neural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance Imaging

https://github.com/wq2012/DecisionForest

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Semantic Context Forests for Learning-Based Knee Cartilage Segmentation in 3D MR Images

https://github.com/wrinklexuan/graph-flow

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Graph Flow: Cross-layer Graph Flow Distillation for Dual Efficient Medical Image Segmentation

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

공식 코드

One-Prompt to Segment All Medical Images

https://github.com/wukevin/babel

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Deep Learning in Single-Cell Analysis

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/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/wyyu0831/san

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SAN-Net: Learning Generalization to Unseen Sites for Stroke Lesion Segmentation with Self-Adaptive Normalization

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

공식 코드

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

https://github.com/xhu248/autosam

공식 코드

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

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