Papers with Code 아카이브

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/arnold-jun/fed-munet

공식 코드

Fed-MUnet: Multi-modal Federated Unet for Brain Tumor Segmentation

https://github.com/aryan-at-ul/electronic-imaging-2025-paper-4492

공식 코드

Image Segmentation: Inducing graph-based learning

https://github.com/asbjrnmunk/mdd-unet

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MDD-UNet: Domain Adaptation for Medical Image Segmentation with Theoretical Guarantees, a Proof of Concept

https://github.com/asgsaeid/KernelizedManifoldMapping

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A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations

https://github.com/ashstuff/dissm

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Deep Implicit Statistical Shape Models for 3D Medical Image Delineation

https://github.com/asif-hanif/vafa

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Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentation

https://github.com/askerlee/segtran

공식 코드

Medical Image Segmentation Using Squeeze-and-Expansion Transformers

https://github.com/astlian9/sam_support

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SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting

https://github.com/aswahd/sam2radiology

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Sam2Rad: A Segmentation Model for Medical Images with Learnable Prompts

https://github.com/aswahd/samradiology

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Sam2Rad: A Segmentation Model for Medical Images with Learnable Prompts

https://github.com/auroua/hrstnet

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High-Resolution Swin Transformer for Automatic Medical Image Segmentation

https://github.com/avakanski/Attention-Enriched-DL-Model-for-Breast-Tumor-Segmentation

공식 코드

Attention Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images

https://github.com/avc2-uestc/qmedsam

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Efficient Quantization-Aware Training on Segment Anything Model in Medical Images and Its Deployment

https://github.com/awadelrahman/medai

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Explainable Medical Image Segmentation via Generative Adversarial Networks and Layer-wise Relevance Propagation

https://github.com/axi404/pmt

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PMT: Progressive Mean Teacher via Exploring Temporal Consistency for Semi-Supervised Medical Image Segmentation

https://github.com/aywi/mprotonet

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MProtoNet: A Case-Based Interpretable Model for Brain Tumor Classification with 3D Multi-parametric Magnetic Resonance Imaging

https://github.com/azadef/ynet

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Y-Net: A Spatiospectral Dual-Encoder Networkfor Medical Image Segmentation

https://github.com/baai-dcai/segvol

공식 코드

SegVol: Universal and Interactive Volumetric Medical Image Segmentation

https://github.com/backpropagator/langdaug

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LangDAug: Langevin Data Augmentation for Multi-Source Domain Generalization in Medical Image Segmentation

https://github.com/bala93/marginloss

공식 코드

Calibrating Segmentation Networks with Margin-based Label Smoothing

https://github.com/bangwayne/verse

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VerSe: Integrating Multiple Queries as Prompts for Versatile Cardiac MRI Segmentation

https://github.com/baobao0926/asp-vmunet

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ASP-VMUNet: Atrous Shifted Parallel Vision Mamba U-Net for Skin Lesion Segmentation

https://github.com/baumgach/PHiSeg-code

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PHiSeg: Capturing Uncertainty in Medical Image Segmentation

https://github.com/bbbbbbzhou/APA2Seg-Net

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Anatomy-guided Multimodal Registration by Learning Segmentation without Ground Truth: Application to Intraprocedural CBCT/MR Liver Segmentation and Registration

https://github.com/bchimagine/real_time_fetal_brain_segmentation

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Deep Learning Framework for Real-time Fetal Brain Segmentation in MRI

https://github.com/beginner-cjh/medvkan

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MedVKAN: Efficient Feature Extraction with Mamba and KAN for Medical Image Segmentation

https://github.com/beileicui/ms-tfal

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Rectifying Noisy Labels with Sequential Prior: Multi-Scale Temporal Feature Affinity Learning for Robust Video Segmentation

https://github.com/benjamintowle/seqsam

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SeqSAM: Autoregressive Multiple Hypothesis Prediction for Medical Image Segmentation using SAM

https://github.com/benjamintowle/simsam

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SimSAM: Zero-shot Medical Image Segmentation via Simulated Interaction

https://github.com/benolmbrt/wcp_miccai

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Robust Conformal Volume Estimation in 3D Medical Images

https://github.com/bidur-khanal/mvaal-medical-images

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M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis Tasks

https://github.com/billhhh/MCKD

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Meta-Learned Modality-Weighted Knowledge Distillation for Robust Multi-Modal Learning with Missing Data

https://github.com/billhhh/ShaSpec

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Multi-modal Learning with Missing Modality via Shared-Specific Feature Modelling

https://github.com/bingogome/samm

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Segment Any Medical Model Extended

https://github.com/binycn/dcpa

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Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation

https://github.com/bio-mlhui/lgrnet

공식 코드

LGRNet: Local-Global Reciprocal Network for Uterine Fibroid Segmentation in Ultrasound Videos

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