Papers with Code 아카이브

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/juntang-zhuang/LadderNet

공식 코드

LadderNet: Multi-path networks based on U-Net for medical image segmentation

https://github.com/juntaojianggavin/lv-unet

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LV-UNet: A Lightweight and Vanilla Model for Medical Image Segmentation

https://github.com/juntaojianggavin/rwkv-unet

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RWKV-UNet: Improving UNet with Long-Range Cooperation for Effective Medical Image Segmentation

https://github.com/juntongkuki/pytorch-dsfnet

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DSFNet: Dual-GCN and Location-fused Self-attention with Weighted Fast Normalized Fusion for Polyps Segmentation

https://github.com/junyuchen245/Semi-supervised_FCM_Loss_for_Segmentation

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Learning Fuzzy Clustering for SPECT/CT Segmentation via Convolutional Neural Networks

https://github.com/junyuchen245/Unsuprevised_Seg_via_CNN

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Medical Image Segmentation via Unsupervised Convolutional Neural Network

https://github.com/junzengz/rmamamba

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A Reverse Mamba Attention Network for Pathological Liver Segmentation

https://github.com/jupitern52/textbrats

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TextBraTS: Text-Guided Volumetric Brain Tumor Segmentation with Innovative Dataset Development and Fusion Module Exploration

https://github.com/jusiro/constrained_anomaly_segmentation

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Constrained unsupervised anomaly segmentation

https://github.com/jusiro/fewshot-finetuning

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Towards Foundation Models and Few-Shot Parameter-Efficient Fine-Tuning for Volumetric Organ Segmentation

https://github.com/justatinydot/bitr-unet

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BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor Segmentation

https://github.com/justlfc03/dfcps

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Semi-supervised Medical Image Segmentation Method Based on Cross-pseudo Labeling Leveraging Strong and Weak Data Augmentation Strategies

https://github.com/justusschock/shapenet

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Super-realtime facial landmark detection and shape fitting by deep regression of shape model parameters

https://github.com/jvwilliams23/pyssam

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pyssam -- a Python library for statistical modelling of biomedical shape and appearance

https://github.com/jwc-rad/mislight

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Knowledge Distillation from Cross Teaching Teachers for Efficient Semi-Supervised Abdominal Organ Segmentation in CT

https://github.com/jwicaksana/fedmix

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FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation

https://github.com/kabbas570/cams-net

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CAMS: Convolution and Attention-Free Mamba-based Cardiac Image Segmentation

https://github.com/kaiko-ai/eva

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Towards Large-Scale Training of Pathology Foundation Models

https://github.com/kaiko-ai/towards_large_pathology_fms

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Towards Large-Scale Training of Pathology Foundation Models

https://github.com/kaiseem/dar-unet

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CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation

https://github.com/kaiseem/slaug

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Rethinking Data Augmentation for Single-source Domain Generalization in Medical Image Segmentation

https://github.com/kakumarabhishek/Illumination-based-Transformations-Skin-Lesion-Segmentation

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Illumination-based Transformations Improve Skin Lesion Segmentation in Dermoscopic Images

https://github.com/kakumarabhishek/MCC-Loss

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Matthews Correlation Coefficient Loss for Deep Convolutional Networks: Application to Skin Lesion Segmentation

https://github.com/kaland313/ssl-medseg

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Self-Supervised Pretraining for 2D Medical Image Segmentation

https://github.com/kalrfou/gctx-unet

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GCtx-UNet: Efficient Network for Medical Image Segmentation

https://github.com/kaminAI/beem

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Boltzmann Exploration Expectation-Maximisation

https://github.com/kamruleee51/Skin-Lesion-Segmentation-Using-Proposed-DSNet

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DSNet: Automatic Dermoscopic Skin Lesion Segmentation

https://github.com/kangningthu/adele

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Adaptive Early-Learning Correction for Segmentation from Noisy Annotations

https://github.com/kathyliu579/contrastivecross-teachingwithregistration

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Learning Semi-Supervised Medical Image Segmentation from Spatial Registration

https://github.com/kathyliu579/cs-unet

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Optimizing Vision Transformers for Medical Image Segmentation

https://github.com/keeplearning-again/matchseg

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MatchSeg: Towards Better Segmentation via Reference Image Matching

https://github.com/kent0n-li/medical-image-segmentation

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nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performance

https://github.com/kerem-cekmeceli/foundation-models-for-medical-imagery

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Do Vision Foundation Models Enhance Domain Generalization in Medical Image Segmentation?

https://github.com/key1589745/decouple_predict

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Decoupling Predictions in Distributed Learning for Multi-Center Left Atrial MRI Segmentation

https://github.com/kibrom2b/LFB-Net

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Learning With Context Feedback Loop for Robust Medical Image Segmentation

https://github.com/kidswithtokens/medical-sam-adapter

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Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

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