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/cgpxy/esknet

공식 코드

ESKNet-An enhanced adaptive selection kernel convolution for breast tumors segmentation

https://github.com/cgpzy/aau-net

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AAU-net: An Adaptive Attention U-net for Breast Lesions Segmentation in Ultrasound Images

https://github.com/chamanis/splitfedzip

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SplitFedZip: Learned Compression for Data Transfer Reduction in Split-Federated Learning

https://github.com/charlesyou999648/MONA

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Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels

https://github.com/charlesyou999648/action

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ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast

https://github.com/charlesyou999648/arco

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Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective

https://github.com/charlesyou999648/morse

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Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts

https://github.com/charrywhite/elastic_interaction_based_loss

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An Elastic Interaction-Based Loss Function for Medical Image Segmentation

https://github.com/charulp2499/fess_loss

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FESS Loss: Feature-Enhanced Spatial Segmentation Loss for Optimizing Medical Image Analysis

https://github.com/charzharr/miccai23-swipe-implicit-segmentation

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SwIPE: Efficient and Robust Medical Image Segmentation with Implicit Patch Embeddings

https://github.com/chen-ziyang/grata

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Gradient Alignment Improves Test-Time Adaptation for Medical Image Segmentation

https://github.com/chen-ziyang/trid

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Treasure in Distribution: A Domain Randomization based Multi-Source Domain Generalization for 2D Medical Image Segmentation

https://github.com/chen-ziyang/vptta

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Each Test Image Deserves A Specific Prompt: Continual Test-Time Adaptation for 2D Medical Image Segmentation

https://github.com/cheng-01037/Self-supervised-Fewshot-Medical-Image-Segmentation

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Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation

https://github.com/cheng-01037/causality-medical-image-domain-generalization

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Causality-inspired Single-source Domain Generalization for Medical Image Segmentation

https://github.com/cheng-01037/probability_calibration_for_ood_mri_segmentation

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Improved post-hoc probability calibration for out-of-domain MRI segmentation

https://github.com/chengyinlee/autoprosam_2024

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AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentation

https://github.com/chengyinlee/mulmodseg_2024

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MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training

https://github.com/chenhong-zhou/OM-Net

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One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor Segmentation

https://github.com/chenzhao2023/upmad_net_brainseg

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UPMAD-Net: A Brain Tumor Segmentation Network with Uncertainty Guidance and Adaptive Multimodal Feature Fusion

https://github.com/cherise215/Cooperative_Training_and_Latent_Space_Data_Augmentation

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Cooperative Training and Latent Space Data Augmentation for Robust Medical Image Segmentation

https://github.com/cherise215/advchain

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Enhancing MR Image Segmentation with Realistic Adversarial Data Augmentation

https://github.com/cherise215/maxstyle

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MaxStyle: Adversarial Style Composition for Robust Medical Image Segmentation

https://github.com/chollette/SEDNet_Shallow-Encoder-Decoder-Network-for-Brain-Tumor-Segmentation

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SEDNet: Shallow Encoder-Decoder Network for Brain Tumor Segmentation

https://github.com/chongqingnosubway/selfreg-unet

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SelfReg-UNet: Self-Regularized UNet for Medical Image Segmentation

https://github.com/chrissyinreallife/kneesegmentwithsam

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Segmentation by registration-enabled SAM prompt engineering using five reference images

https://github.com/christophreich1996/oss-net

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OSS-Net: Memory Efficient High Resolution Semantic Segmentation of 3D Medical Data

https://github.com/christophreich1996/yeast-in-microstructures-dataset

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An Instance Segmentation Dataset of Yeast Cells in Microstructures

https://github.com/chula-pic-lab/chula-rbc-12-dataset

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Red Blood Cell Segmentation with Overlapping Cell Separation and Classification on Imbalanced Dataset

https://github.com/chunyuyuan/mucm-net

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MUCM-Net: A Mamba Powered UCM-Net for Skin Lesion Segmentation

https://github.com/chunyuyuan/ucm-net

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UCM-Net: A Lightweight and Efficient Solution for Skin Lesion Segmentation using MLP and CNN

https://github.com/chuyun-shen/sam_2_medical_3d

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Interactive 3D Medical Image Segmentation with SAM 2

https://github.com/chy-upc/abd

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Adaptive Bidirectional Displacement for Semi-Supervised Medical Image Segmentation

https://github.com/cityu-aim-group/jcas

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Joint Class-Affinity Loss Correction for Robust Medical Image Segmentation with Noisy Labels

https://github.com/clguo/CAR-UNet

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Channel Attention Residual U-Net for Retinal Vessel Segmentation

https://github.com/clguo/RSAN

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Residual Spatial Attention Network for Retinal Vessel Segmentation

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