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Image Segmentation

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하위 태스크: 1
평가표: 13
보존된 평가 기록: 34

Image Segmentation is a computer vision task that involves dividing an image into multiple segments or regions, each of which corresponds to a different object or part of an object. The goal of image segmentation is to assign a unique label or category to each pixel in the image, so that pixels with similar attributes are grouped together.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/juliawolleb/diffusion-based-segmentation

공식 코드

Diffusion Models for Implicit Image Segmentation Ensembles

https://github.com/juliuswang-7/ce_block

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Contextual Embedding Learning to Enhance 2D Networks for Volumetric Image Segmentation

https://github.com/jun-jie-shi/passion

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PASSION: Towards Effective Incomplete Multi-Modal Medical Image Segmentation with Imbalanced Missing Rates

https://github.com/junelin2333/languidemedseg-miccai2023

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Ariadne's Thread:Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray images

https://github.com/juntang-zhuang/LadderNet

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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/junyuchen245/ViT-V-Net_for_3D_Image_Registration_Pytorch

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ViT-V-Net: Vision Transformer for Unsupervised Volumetric Medical Image Registration

https://github.com/junzis/contrail-net

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Flight Contrail Segmentation via Augmented Transfer Learning with Novel SR Loss Function in Hough Space

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/fewshot-finetuning

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Towards Foundation Models and Few-Shot Parameter-Efficient Fine-Tuning for Volumetric Organ 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/justlfc03/scunet-plusplus

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SCUNet++: Swin-UNet and CNN Bottleneck Hybrid Architecture with Multi-Fusion Dense Skip Connection for Pulmonary Embolism CT Image Segmentation

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/jw-chae/sjtu

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SJTU:Spatial judgments in multimodal models towards unified segmentation through coordinate detection

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/kabbas570/compseg-metadata

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Compositional Segmentation of Cardiac Images Leveraging Metadata

https://github.com/kabbas570/crocnet

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Crop and Couple: cardiac image segmentation using interlinked specialist networks

https://github.com/kainmueller-lab/arctique

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Arctique: An artificial histopathological dataset unifying realism and controllability for uncertainty quantification

https://github.com/kainmueller-lab/tw-autoencoder

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Model Guidance via Explanations Turns Image Classifiers into Segmentation 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/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/kalrfou/swint-pretrained-microscopy-models

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Transfer Learning for Microstructure Segmentation with CS-UNet: A Hybrid Algorithm with Transformer and CNN Encoders

https://github.com/kanezaki/pytorch-unsupervised-segmentation-tip

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Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering

https://github.com/kanji95/SHNET

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Comprehensive Multi-Modal Interactions for Referring Image Segmentation

https://github.com/kanji95/Talk2car-Refseg

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Grounding Linguistic Commands to Navigable Regions

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

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