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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/Barrett-python/DuAT

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DuAT: Dual-Aggregation Transformer Network for Medical Image Segmentation

https://github.com/Beckschen/3D-TransUNet

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3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers

https://github.com/Beckschen/TransUNet

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TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

https://github.com/BirkTorpmannHagen/SegmentationConsistencyTraining

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Segmentation Consistency Training: Out-of-Distribution Generalization for Medical Image Segmentation

https://github.com/CAMERA-MRI/SPARK2023/tree/main/SPARK_BTS_KIFARU

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Generative Style Transfer for MRI Image Segmentation: A Case of Glioma Segmentation in Sub-Saharan Africa

https://github.com/CSCYQJ/LOCATION-SENSITIVE-LOCAL-PROTOTYPE-NETWORK

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A Location-Sensitive Local Prototype Network for Few-Shot Medical Image Segmentation

https://github.com/CUMC-Yuan-Lab/GCML/tree/main/SANet/BraTS

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Automatic Brain Tumor Segmentation with Scale Attention Network

https://github.com/CVRL/RaspberryPiOpenSourceIris

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Open Source Iris Recognition Hardware and Software with Presentation Attack Detection

https://github.com/Captain-Hong/UDA-via-joint-adversarial-learning-and-self-learning

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Unsupervised domain adaptation for cross-modality liver segmentation via joint adversarial learning and self-learning

https://github.com/ChenLiu-1996/CUTS

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CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation

https://github.com/China-LiuXiaopeng/BraTS-DMFNet

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3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI

https://github.com/ChristophReich1996/Cell-DETR

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Attention-Based Transformers for Instance Segmentation of Cells in Microstructures

https://github.com/Confusezius/unet-lits-2d-pipeline

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Liver Lesion Segmentation with slice-wise 2D Tiramisu and Tversky loss function

https://github.com/DIAGNijmegen/neural-odes-segmentation

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Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands

https://github.com/DIDSR/ssynth-release

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S-SYNTH: Knowledge-Based, Synthetic Generation of Skin Images

https://github.com/DLTK/models

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NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines

https://github.com/Daniyanaj/vMixer

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Improving 3D Medical Image Segmentation at Boundary Regions using Local Self-attention and Global Volume Mixing

https://github.com/DarkoBomer/VCA-Net

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Stroke Lesion Segmentation with Visual Cortex Anatomy Alike Neural Nets

https://github.com/DebeshJha/2020-CBMS-DoubleU-Net

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DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation

https://github.com/DebeshJha/ColonSegNet

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Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning

https://github.com/DebeshJha/Kvasir-Instrument

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Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy

https://github.com/DebeshJha/NanoNet

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NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy

https://github.com/DebeshJha/ResUNet-with-CRF-and-TTA

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A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation

https://github.com/DebeshJha/ResUNetplusplus

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ResUNet++: An Advanced Architecture for Medical Image Segmentation

https://github.com/Deep-MI/FastSurfer

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FastSurfer -- A fast and accurate deep learning based neuroimaging pipeline

https://github.com/DengPingFan/Polyp-PVT

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Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers

https://github.com/DengPingFan/PraNet

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PraNet: Parallel Reverse Attention Network for Polyp Segmentation

https://github.com/Digital-Dermatology/t-loss

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Robust T-Loss for Medical Image Segmentation

https://github.com/EKirschbaum/DISCo

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DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging

https://github.com/ESandML/FCBFormer

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FCN-Transformer Feature Fusion for Polyp Segmentation

https://github.com/EagleMIT/EMKD

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Efficient Medical Image Segmentation Based on Knowledge Distillation

https://github.com/FETS-AI/Challenge

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The Federated Tumor Segmentation (FeTS) Challenge

https://github.com/FETS-AI/Front-End

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The Federated Tumor Segmentation (FeTS) Challenge

https://github.com/FZJ-INM1-BDA/celldetection

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Contour Proposal Networks for Biomedical Instance Segmentation

https://github.com/FengheTan9/Medical-Image-Segmentation-Benchmarks

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CMUNeXt: An Efficient Medical Image Segmentation Network based on Large Kernel and Skip Fusion

https://github.com/FengheTan9/Multi-Level-Global-Context-Cross-Consistency

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Multi-Level Global Context Cross Consistency Model for Semi-Supervised Ultrasound Image Segmentation with Diffusion Model

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