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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/steve-zeyu-zhang/SegReg

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SegReg: Segmenting OARs by Registering MR Images and CT Annotations

https://github.com/stevezs315/attukan

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Novel Extraction of Discriminative Fine-Grained Feature to Improve Retinal Vessel Segmentation

https://github.com/strivezs/alps

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ALPS: An Auto-Labeling and Pre-training Scheme for Remote Sensing Segmentation With Segment Anything Model

https://github.com/sun-1024/da-transunet

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DA-TransUNet: Integrating Spatial and Channel Dual Attention with Transformer U-Net for Medical Image Segmentation

https://github.com/sunalbert/Sequential-patch-based-segmentation

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Interactive Medical Image Segmentation via Point-Based Interaction and Sequential Patch Learning

https://github.com/sunfan-bvb/boundarydouloss

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Boundary Difference Over Union Loss For Medical Image Segmentation

https://github.com/sunjesse/shape-attentive-unet

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SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation

https://github.com/super-tadory/primgeoseg

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Primitive Geometry Segment Pre-training for 3D Medical Image Segmentation

https://github.com/suuujm/spss

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Self-Paced Sample Selection for Barely-Supervised Medical Image Segmentation

https://github.com/sveinnpalsson/semivaebrats

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Semi-Supervised Variational Autoencoder for Survival Prediction

https://github.com/swedfr/memorizingsam

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Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer

https://github.com/syanng/blazeneo

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BlazeNeo: Blazing fast polyp segmentation and neoplasm detection

https://github.com/syshin1014/VGN

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Deep Vessel Segmentation By Learning Graphical Connectivity

https://github.com/tabrisrei/isles22_ensemble

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A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

https://github.com/taigw/GeodisTK

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MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning

https://github.com/takimailto/BerDiff

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BerDiff: Conditional Bernoulli Diffusion Model for Medical Image Segmentation

https://github.com/takimailto/hidiff

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HiDiff: Hybrid Diffusion Framework for Medical Image Segmentation

https://github.com/tanyanair/segmentation_uncertainty

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Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation

https://github.com/taozh2017/decseg

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Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation

https://github.com/taozh2017/knowsam

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Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation

https://github.com/tbdair/fedperlv1.0

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ROAM: Random Layer Mixup for Semi-Supervised Learning in Medical Imaging

https://github.com/tbuikr/3D_DenseSeg

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3D Densely Convolutional Networks for Volumetric Segmentation

https://github.com/tbwa233/ada-sam

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Autoadaptive Medical Segment Anything Model

https://github.com/tbwa233/polycl

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Domain and Task-Focused Example Selection for Data-Efficient Contrastive Medical Image Segmentation

https://github.com/tbwa233/sam-mix

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Annotation-Efficient Task Guidance for Medical Segment Anything

https://github.com/tdlhl/LoRKD

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LoRKD: Low-Rank Knowledge Decomposition for Medical Foundation Models

https://github.com/tensorflow/models

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Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

https://github.com/tfboys-lzz/mpscl

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Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation

https://github.com/tgrex6/unetvl

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UNetVL: Enhancing 3D Medical Image Segmentation with Chebyshev KAN Powered Vision-LSTM

https://github.com/thanos-db/fullyconvolutionaltransformer

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The Fully Convolutional Transformer for Medical Image Segmentation

https://github.com/thetna/medical_image_segmentation

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Histogram of Oriented Gradients Meet Deep Learning: A Novel Multi-task Deep Network for Medical Image Semantic Segmentation

https://github.com/thisgame42/hybrid-teacher

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Hybrid Dual Mean-Teacher Network With Double-Uncertainty Guidance for Semi-Supervised Segmentation of MRI Scans

https://github.com/thomasbudd/ctarr

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CTARR: A fast and robust method for identifying anatomical regions on CT images via atlas registration

https://github.com/tianrun-chen/xLSTM-UNet-PyTorch

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xLSTM-UNet can be an Effective 2D & 3D Medical Image Segmentation Backbone with Vision-LSTM (ViL) better than its Mamba Counterpart

https://github.com/tianzetang0504/ukan-ep

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UKAN-EP: Enhancing U-KAN with Efficient Attention and Pyramid Aggregation for 3D Multi-Modal MRI Brain Tumor Segmentation

https://github.com/tiffany9056/uu-mamba

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UU-Mamba: Uncertainty-aware U-Mamba for Cardiac Image Segmentation

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