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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/akuehlka/xai4b_tlpim

공식 코드

Interpretable Deep Learning-Based Forensic Iris Segmentation and Recognition

https://github.com/al3x-o-o-hung/csam

공식 코드

CSAM: A 2.5D Cross-Slice Attention Module for Anisotropic Volumetric Medical Image Segmentation

https://github.com/alainjungo/reliability-challenges-uncertainty

공식 코드

Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation

https://github.com/aleemsidra/SaLIP

공식 코드

Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image Segmentation

https://github.com/alessandro-f/dif-fuse

공식 코드

Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images

https://github.com/alexanderhberger/topo-pitfalls

공식 코드

Pitfalls of topology-aware image segmentation

https://github.com/alexanderjaus/autopet3_datadiet

공식 코드

Data Diet: Can Trimming PET/CT Datasets Enhance Lesion Segmentation?

https://github.com/alexklwong/subpixel-embedding-segmentation

공식 코드

Small Lesion Segmentation in Brain MRIs with Subpixel Embedding

https://github.com/alexyouxin/explicit-shape-priors

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Learning with Explicit Shape Priors for Medical Image Segmentation

https://github.com/alfredqin/db-sam

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DB-SAM: Delving into High Quality Universal Medical Image Segmentation

https://github.com/alibaba-damo-academy/alice

공식 코드

Anatomical Invariance Modeling and Semantic Alignment for Self-supervised Learning in 3D Medical Image Analysis

https://github.com/alibaba-damo-academy/ct-sam3d

공식 코드

Towards a Comprehensive, Efficient and Promptable Anatomic Structure Segmentation Model using 3D Whole-body CT Scans

https://github.com/alibaba-damo-academy/pixel-lesion-patient-network

공식 코드

Liver Tumor Screening and Diagnosis in CT with Pixel-Lesion-Patient Network

https://github.com/alikaraali/dr-vnet

공식 코드

DR-VNet: Retinal Vessel Segmentation via Dense Residual UNet

https://github.com/americofmoliveira/VesselSegmentation_ESWA

공식 코드

Retinal Vessel Segmentation based on Fully Convolutional Networks

https://github.com/amirhossein-kz/hiformer

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HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation

https://github.com/amshaker/unetr_plus_plus

공식 코드

UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation

https://github.com/andre-ye/confcontours-data

공식 코드

Confidence Contours: Uncertainty-Aware Annotation for Medical Semantic Segmentation

https://github.com/andreped/livermask

공식 코드

Robust End-to-End Focal Liver Lesion Detection using Unregistered Multiphase Computed Tomography Images

https://github.com/anissa218/autopet_nnunet

공식 코드

Autopet Challenge 2023: nnUNet-based whole-body 3D PET-CT Tumour Segmentation

https://github.com/anjilab/secure-private-ai

공식 코드

Adversarial Robustness Analysis of Vision-Language Models in Medical Image Segmentation

https://github.com/annawundram/PerformanceRangePrediction

공식 코드

Conformal Performance Range Prediction for Segmentation Output Quality Control

https://github.com/annazzz-zxq/curriculum-prompting

공식 코드

Curriculum Prompting Foundation Models for Medical Image Segmentation

https://github.com/anonymous4545/jers

공식 코드

One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data

https://github.com/anpc849/QMaxViT-Unet

공식 코드

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images

https://github.com/anthonyweidai/SvANet

공식 코드

Exploiting Scale-Variant Attention for Segmenting Small Medical Objects

https://github.com/apple1986/LeViT_UNet

공식 코드

LeViT-UNet: Make Faster Encoders with Transformer for Medical Image Segmentation

https://github.com/apple1986/samatch

공식 코드

A SAM-guided and Match-based Semi-Supervised Segmentation Framework for Medical Imaging

https://github.com/arbellea/DeepCellSeg

공식 코드

Microscopy Cell Segmentation via Adversarial Neural Networks

https://github.com/arbellea/LSTM-UNet

공식 코드

Microscopy Cell Segmentation via Convolutional LSTM Networks

https://github.com/arcadelab/DeepDRR

공식 코드

SyntheX: Scaling Up Learning-based X-ray Image Analysis Through In Silico Experiments

https://github.com/arcadelab/counterfactual_diffusion

공식 코드

Promptable Counterfactual Diffusion Model for Unified Brain Tumor Segmentation and Generation with MRIs

https://github.com/arcadelab/fastsam3d

공식 코드

FastSAM3D: An Efficient Segment Anything Model for 3D Volumetric Medical Images

https://github.com/arcadelab/fastsam3d_slicer

공식 코드

FastSAM-3DSlicer: A 3D-Slicer Extension for 3D Volumetric Segment Anything Model with Uncertainty Quantification

https://github.com/arnab39/FewShot_GAN-Unet3D

공식 코드

Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning

https://github.com/arnaudjudge/rl4seg

공식 코드

Domain Adaptation of Echocardiography Segmentation Via Reinforcement Learning

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