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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/nabsabraham/focal-tversky-unet

공식 코드

A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation

https://github.com/nachifur/LLPC

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Local Label Point Correction for Edge Detection of Overlapping Cervical Cells

https://github.com/naga-karthik/continual-learning-ms

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Segmentation of Multiple Sclerosis Lesions across Hospitals: Learn Continually or Train from Scratch?

https://github.com/naisops/CTranS

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ConvTransSeg: A Multi-resolution Convolution-Transformer Network for Medical Image Segmentation

https://github.com/naivete5656/WSISPDR

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Weakly Supervised Cell Instance Segmentation by Propagating from Detection Response

https://github.com/nanase1025/m-sam

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Mask-Enhanced Segment Anything Model for Tumor Lesion Semantic Segmentation

https://github.com/nanyomy/cmmas

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Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label Fusion

https://github.com/nasim-aust/braintumor-segmentation-3dunet-brats

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Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysis

https://github.com/nataliiamolch/interpret-lesion-unc

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Interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis

https://github.com/nataliiamolch/ndsc

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Tackling Bias in the Dice Similarity Coefficient: Introducing nDSC for White Matter Lesion Segmentation

https://github.com/nayutayuki/lightweight-nmode-decoders-for-u-like-networks

공식 코드

A Lightweight U-like Network Utilizing Neural Memory Ordinary Differential Equations for Slimming the Decoder

https://github.com/neerakara/test-time-adaptable-neural-networks-for-domain-generalization

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Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation

https://github.com/neerakara/tta-1d-density-matching

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A Field of Experts Prior for Adapting Neural Networks at Test Time

https://github.com/negin-ghamsarian/transformation-invariant-self-training-miccai23

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Domain Adaptation for Medical Image Segmentation using Transformation-Invariant Self-Training

https://github.com/neheller/labels18

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Imperfect Segmentation Labels: How Much Do They Matter?

https://github.com/nekopii/tjdr

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TJDR: A High-Quality Diabetic Retinopathy Pixel-Level Annotation Dataset

https://github.com/neuro-ml/domain_shift_anatomy

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Anatomy of Domain Shift Impact on U-Net Layers in MRI Segmentation

https://github.com/neuro-ml/inverse_weighting

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Universal Loss Reweighting to Balance Lesion Size Inequality in 3D Medical Image Segmentation

https://github.com/neuronets/kwyk

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Knowing what you know in brain segmentation using Bayesian deep neural networks

https://github.com/neuropoly/domainadaptation

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Unsupervised domain adaptation for medical imaging segmentation with self-ensembling

https://github.com/neuropoly/ivado-medical-imaging

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Automatic segmentation of spinal multiple sclerosis lesions: How to generalize across MRI contrasts?

https://github.com/ngaggion/HybridGNet

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Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis

https://github.com/ngaggion/chest-xray-landmark-dataset

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Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis

https://github.com/ngaggion/ubd_sourcecode

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Unsupervised bias discovery in medical image segmentation

https://github.com/nguyenhoangthuan99/RaBiT

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RaBiT: An Efficient Transformer using Bidirectional Feature Pyramid Network with Reverse Attention for Colon Polyp Segmentation

https://github.com/nhjydywd/octa-frnet

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An Accurate and Efficient Neural Network for OCTA Vessel Segmentation and a New Dataset

https://github.com/nibtehaz/MultiResUNet

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MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation

https://github.com/nikhilroxtomar/DDANet

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DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation

https://github.com/nikhilroxtomar/fanet

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FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation

https://github.com/nikhilroxtomar/tganet

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TGANet: Text-guided attention for improved polyp segmentation

https://github.com/nikhilroxtomar/transresunet

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TransResU-Net: Transformer based ResU-Net for Real-Time Colonoscopy Polyp Segmentation

https://github.com/niloide/implicit_segmentation

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NISF: Neural Implicit Segmentation Functions

https://github.com/nina-weng/shortcut_skinseg

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Shortcut Learning in Medical Image Segmentation

https://github.com/nitr098/attswinunet

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Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentation

https://github.com/nitr098/awesome-u-net

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Medical Image Segmentation Review: The success of U-Net

https://github.com/nkicsl/Fundus_Review

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Applications of Deep Learning in Fundus Images: A Review

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