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/lofrienger/curri-afda

공식 코드

Curriculum-Based Augmented Fourier Domain Adaptation for Robust Medical Image Segmentation

https://github.com/lofrienger/s2me

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S$^2$ME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-supervised Polyp Segmentation

https://github.com/logan-0623/pg-sam

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PG-SAM: Prior-Guided SAM with Medical for Multi-organ Segmentation

https://github.com/looooongChen/MRBrainS-Brain-Segmentation

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MixNet: Multi-modality Mix Network for Brain Segmentation

https://github.com/looooongChen/instance_segmentation_with_pixel_embeddings

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Instance Segmentation of Biomedical Images with an Object-aware Embedding Learned with Local Constraints

https://github.com/louey233/toward-mutual-information

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Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization

https://github.com/louisdo2108/acs-nnu-net

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Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal Embedding

https://github.com/lseventeen/phtrans

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PHTrans: Parallelly Aggregating Global and Local Representations for Medical Image Segmentation

https://github.com/lswzjuer/NAS-WDAN

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MixSearch: Searching for Domain Generalized Medical Image Segmentation Architectures

https://github.com/lucasfidon/feta-inference

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Partial supervision for the FeTA challenge 2021

https://github.com/lucasfidon/trabit_brats2021

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Generalized Wasserstein Dice Loss, Test-time Augmentation, and Transformers for the BraTS 2021 challenge

https://github.com/lucasfidon/trustworthy-ai-fetal-brain-segmentation

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A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation

https://github.com/luo-lorry/Stylized-Medical-Segmentation

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Structure-Aware Stylized Image Synthesis for Robust Medical Image Segmentation

https://github.com/luxneuroimage/dbsegment

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DBSegment: Fast and robust segmentation of deep brain structures -- Evaluation of transportability across acquisition domains

https://github.com/lw8807001/unimos

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UniMOS: A Universal Framework For Multi-Organ Segmentation Over Label-Constrained Datasets

https://github.com/lwhyc/oneshot_weaklyseg

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One-shot Weakly-Supervised Segmentation in Medical Images

https://github.com/lwpyh/ProMaC_code

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Leveraging Hallucinations to Reduce Manual Prompt Dependency in Promptable Segmentation

https://github.com/lydia-yllu/diff-vps

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Diff-VPS: Video Polyp Segmentation via a Multi-task Diffusion Network with Adversarial Temporal Reasoning

https://github.com/lyn1874/region_based_active_learning

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On uncertainty estimation in active learning for image segmentation

https://github.com/lyxdlii/procns

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ProCNS: Progressive Prototype Calibration and Noise Suppression for Weakly-Supervised Medical Image Segmentation

https://github.com/lzeeorno/AFFSegNet

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AFFSegNet: Adaptive Feature Fusion Segmentation Network for Microtumors and Multi-Organ Segmentation

https://github.com/lzeeorno/SMAFormer

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SMAFormer: Synergistic Multi-Attention Transformer for Medical Image Segmentation

https://github.com/lzeeorno/assnet

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AFFSegNet: Adaptive Feature Fusion Segmentation Network for Microtumors and Multi-Organ Segmentation

https://github.com/lzeeorno/lagrange-duality-and-cmaformer

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Lagrange Duality and Compound Multi-Attention Transformer for Semi-Supervised Medical Image Segmentation

https://github.com/lzh19961031/pdcr_uafr-mis

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Contrastive and Selective Hidden Embeddings for Medical Image Segmentation

https://github.com/lzl199704/vis-mae

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VIS-MAE: An Efficient Self-supervised Learning Approach on Medical Image Segmentation and Classification

https://github.com/m3dv/diffatlas

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DiffAtlas: GenAI-fying Atlas Segmentation via Image-Mask Diffusion

https://github.com/m3dv/lefusion

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LeFusion: Controllable Pathology Synthesis via Lesion-Focused Diffusion Models

https://github.com/mabulnaga/automatic-placenta-segmentation

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Automatic Segmentation of the Placenta in BOLD MRI Time Series

https://github.com/mafortin/GOUHFI

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GOUHFI: a novel contrast- and resolution-agnostic segmentation tool for Ultra-High Field MRI

https://github.com/maggiebasta/lung-cancer-thesis

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Automated Segmentation and Recurrence Risk Prediction of Surgically Resected Lung Tumors with Adaptive Convolutional Neural Networks

https://github.com/majid9418/eresu-net

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Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learning

https://github.com/maloadba/mgenseg_2d

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M-GenSeg: Domain Adaptation For Target Modality Tumor Segmentation With Annotation-Efficient Supervision

https://github.com/manem-lab/lung-ddpm

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Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image Synthesis

https://github.com/mansoorhayat777/attention-ghostunetplusplus

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Attention GhostUNet++: Enhanced Segmentation of Adipose Tissue and Liver in CT Images

https://github.com/marcdcfischer/punet

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Prompt Tuning for Parameter-efficient Medical Image Segmentation

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