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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/aaroncao06/MedSegMamba

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MedSegMamba: 3D CNN-Mamba Hybrid Architecture for Brain Segmentation

https://github.com/abdul2706/MaxViT-UNet

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MaxViT-UNet: Multi-Axis Attention for Medical Image Segmentation

https://github.com/abhi4ssj/QuickNATv2

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QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy

https://github.com/abhineet123/ipsc_prediction

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Towards Early Prediction of Human iPSC Reprogramming Success

https://github.com/ad12/DOSMA

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The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset

https://github.com/adalca/neuron

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Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation

https://github.com/adlsn/sli2volplus

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Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network

https://github.com/adnan-cas/atrouslora

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VesselSAM: Leveraging SAM for Aortic Vessel Segmentation with AtrousLoRA

https://github.com/aecelaya/MIST

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PocketNet: A Smaller Neural Network for Medical Image Analysis

https://github.com/aecelaya/gen-surf-loss

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A Generalized Surface Loss for Reducing the Hausdorff Distance in Medical Imaging Segmentation

https://github.com/aecelaya/mg-nets

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FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures For Medical Imaging Segmentation

https://github.com/agaldran/lesion_losses_ood

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On the Optimal Combination of Cross-Entropy and Soft Dice Losses for Lesion Segmentation with Out-of-Distribution Robustness

https://github.com/agaldran/lwnet

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The Little W-Net That Could: State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models

https://github.com/agis85/multimodal_segmentation

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Disentangle, align and fuse for multimodal and semi-supervised image segmentation

https://github.com/agis85/spatial_factorisation

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Factorised spatial representation learning: application in semi-supervised myocardial segmentation

https://github.com/agnalt/window-shifting

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View it like a radiologist: Shifted windows for deep learning augmentation of CT images

https://github.com/agosl/Orthogonal_Ensemble_Networks

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Orthogonal Ensemble Networks for Biomedical Image Segmentation

https://github.com/ahatamiz/dals

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Deep Active Lesion Segmentation

https://github.com/ahukui/BOWDANet

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Boundary-weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation

https://github.com/ahukui/SIPNet

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Selective Information Passing for MR/CT Image Segmentation

https://github.com/ahxmeds/autopet2024

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AutoPET Challenge III: Testing the Robustness of Generalized Dice Focal Loss trained 3D Residual UNet for FDG and PSMA Lesion Segmentation from Whole-Body PET/CT Images

https://github.com/ahxmeds/autosegnet

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Generalized Dice Focal Loss trained 3D Residual UNet for Automated Lesion Segmentation in Whole-Body FDG PET/CT Images

https://github.com/ahxmeds/autosegnet2024

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AutoPET Challenge III: Testing the Robustness of Generalized Dice Focal Loss trained 3D Residual UNet for FDG and PSMA Lesion Segmentation from Whole-Body PET/CT Images

https://github.com/ai-med/halos

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HALOS: Hallucination-free Organ Segmentation after Organ Resection Surgery

https://github.com/ai-med/squeeze_and_excitation

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`Project & Excite' Modules for Segmentation of Volumetric Medical Scans

https://github.com/ai4colonoscopy/pranet-v2

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PraNet-V2: Dual-Supervised Reverse Attention for Medical Image Segmentation

https://github.com/aiconslab/3dino

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A generalizable 3D framework and model for self-supervised learning in medical imaging

https://github.com/aielte-research/MedSegPretrainImageNet

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Enhancing pretraining efficiency for medical image segmentation via transferability metrics

https://github.com/aieson/crossmatch

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CrossMatch: Enhance Semi-Supervised Medical Image Segmentation with Perturbation Strategies and Knowledge Distillation

https://github.com/aigeeksgroup/sss

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SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation

https://github.com/aijinrjinr/mlb-seg

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Consistency-guided Meta-Learning for Bootstrapping Semi-Supervised Medical Image Segmentation

https://github.com/aim-lab/LUNet

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LUNet: Deep Learning for the Segmentation of Arterioles and Venules in High Resolution Fundus Images

https://github.com/aimansnigdha/ambiguous-medical-image-segmentation-using-diffusion-models

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Ambiguous Medical Image Segmentation using Diffusion Models

https://github.com/aitechlabcn/neunet

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More complex encoder is not all you need

https://github.com/ajoshiusc/brainseg

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Learning from imperfect training data using a robust loss function: application to brain image segmentation

https://github.com/ajoshiusc/qrvae

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Deep Quantile Regression for Uncertainty Estimation in Unsupervised and Supervised Lesion Detection

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