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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/parisafarmanifard/iris-sam

공식 코드

Iris-SAM: Iris Segmentation Using a Foundation Model

https://github.com/pascalcpp/sdcl

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SDCL: Students Discrepancy-Informed Correction Learning for Semi-supervised Medical Image Segmentation

https://github.com/pashtari/deconver

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Deconver: A Deconvolutional Network for Medical Image Segmentation

https://github.com/pashtari/factorizer

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Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image Segmentation

https://github.com/pbizopoulos/comprehensive-comparison-of-deep-learning-models-for-lung-and-covid-19-lesion-segmentation-in-ct

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Comprehensive Comparison of Deep Learning Models for Lung and COVID-19 Lesion Segmentation in CT scans

https://github.com/pedrohtg/weasel

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Learning to Segment Medical Images from Few-Shot Sparse Labels

https://github.com/peirong26/Brain-ID

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Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging

https://github.com/pengchengshi1220/nextou

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NexToU: Efficient Topology-Aware U-Net for Medical Image Segmentation

https://github.com/pengchihan/binaryimagedownsampling

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Topology-Preserving Downsampling of Binary Images

https://github.com/perceptioncomputinglab/instance2022

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The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge

https://github.com/perslev/MultiPlanarUNet

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One Network to Segment Them All: A General, Lightweight System for Accurate 3D Medical Image Segmentation

https://github.com/peteryyzhang/few-shot-self-prompt-sam

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Self-Prompting Large Vision Models for Few-Shot Medical Image Segmentation

https://github.com/pfriedri/cwdm

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cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image Synthesis

https://github.com/philip-mueller/equivariant-deep-dmri

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Rotation-Equivariant Deep Learning for Diffusion MRI

https://github.com/piooip/ud-mamba

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UD-Mamba: A pixel-level uncertainty-driven Mamba model for medical image segmentation

https://github.com/pjsjongsung/PUMBA

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Skull stripping with purely synthetic data

https://github.com/plemeri/UACANet

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UACANet: Uncertainty Augmented Context Attention for Polyp Segmentation

https://github.com/poiuohke/UDC-Net

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Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images

https://github.com/pooya-mohammadi/unet-skin-cancer

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U-Net-based Models for Skin Lesion Segmentation: More Attention and Augmentation

https://github.com/pranavsinghps1/dedl

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A Data-Efficient Deep Learning Framework for Segmentation and Classification of Histopathology Images

https://github.com/pranavsinghps1/s4mi

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Shifting to Machine Supervision: Annotation-Efficient Semi and Self-Supervised Learning for Automatic Medical Image Segmentation and Classification

https://github.com/prantik-pdeb/best-les

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BeSt-LeS: Benchmarking Stroke Lesion Segmentation using Deep Supervision

https://github.com/precision-vision/oed-classification-segmentation

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An Attention Based Pipeline for Identifying Pre-Cancer Lesions in Head and Neck Clinical Images

https://github.com/prerakmody/bayesuncertainty-error-correspondence

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Improving Uncertainty-Error Correspondence in Deep Bayesian Medical Image Segmentation

https://github.com/primebo1/FAMNet

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FAMNet: Frequency-aware Matching Network for Cross-domain Few-shot Medical Image Segmentation

https://github.com/prlab21/maxvit-unet

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

https://github.com/programmerhyy/cm-mlp

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CM-MLP: Cascade Multi-scale MLP with Axial Context Relation Encoder for Edge Segmentation of Medical Image

https://github.com/project-monai/research-contributions

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UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image Segmentation

https://github.com/prowontheus/isd-mae

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Intensity-Spatial Dual Masked Autoencoder for Multi-Scale Feature Learning in Chest CT Segmentation

https://github.com/pykao/ISLES2017-mRS-prediction

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Predicting Clinical Outcome of Stroke Patients with Tractographic Feature

https://github.com/python-arch/biombz-optimizing-brain-tumor-segmentation-with-mednext-brats-2024-ssa-and-pediatrics

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Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and Pediatrics

https://github.com/qd-seg/quickdraw

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QuickDraw: Fast Visualization, Analysis and Active Learning for Medical Image Segmentation

https://github.com/qdaiing/fedmema

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Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentation

https://github.com/qintianjian-lab/ras4dg

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Medical Image Segmentation via Single-Source Domain Generalization with Random Amplitude Spectrum Synthesis

https://github.com/qiukunpeng/siamese-diffusion

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Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and Segmentation

https://github.com/qiumuyang/siab

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The Devil is in the Statistics: Mitigating and Exploiting Statistics Difference for Generalizable Semi-supervised Medical Image Segmentation

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