Papers with Code 아카이브

Medical Image Segmentation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

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/importzl/semantic_segmentation

공식 코드

Generative AI Enables Medical Image Segmentation in Ultra Low-Data Regimes

https://github.com/imran-maia/ibsr_18_bratseg_deep_learning

공식 코드

Comparative Study of Probabilistic Atlas and Deep Learning Approaches for Automatic Brain Tissue Segmentation from MRI Using N4 Bias Field Correction and Anisotropic Diffusion Pre-processing Techniques

https://github.com/instanseg/instanseg

공식 코드

InstanSeg: an embedding-based instance segmentation algorithm optimized for accurate, efficient and portable cell segmentation

https://github.com/ipmi-nwu/helpnet

공식 코드

HELPNet: Hierarchical Perturbations Consistency and Entropy-guided Ensemble for Scribble Supervised Medical Image Segmentation

https://github.com/itmo-mmrm-lab/cellpose

공식 코드

Cellpose+, a morphological analysis tool for feature extraction of stained cell images

https://github.com/itruonghai/segtransvae

공식 코드

SegTransVAE: Hybrid CNN -- Transformer with Regularization for medical image segmentation

https://github.com/ituvisionlab/Uncertainty-Based-Dynamic-Graph-Neighborhoods

공식 코드

Uncertainty-Based Dynamic Graph Neighborhoods For Medical Segmentation

https://github.com/ivadomed/ms-challenge-2021

공식 코드

Team NeuroPoly: Description of the Pipelines for the MICCAI 2021 MS New Lesions Segmentation Challenge

https://github.com/ivezakis/effisegnet

공식 코드

EffiSegNet: Gastrointestinal Polyp Segmentation through a Pre-Trained EfficientNet-based Network with a Simplified Decoder

https://github.com/iweisskohl/deep-evidential-fusion

공식 코드

Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation

https://github.com/iweisskohl/evidence-fusion-with-contextual-discounting

공식 코드

Evidence fusion with contextual discounting for multi-modality medical image segmentation

https://github.com/j-morano/multimodal-ssl-fpn

공식 코드

Self-supervised learning via inter-modal reconstruction and feature projection networks for label-efficient 3D-to-2D segmentation

https://github.com/j-morano/rrwnet

공식 코드

RRWNet: Recursive Refinement Network for effective retinal artery/vein segmentation and classification

https://github.com/jacobzhaoziyuan/ACT-Net

공식 코드

ACT-Net: Asymmetric Co-Teacher Network for Semi-supervised Memory-efficient Medical Image Segmentation

https://github.com/jacobzhaoziyuan/MMGL

공식 코드

MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image Segmentation

https://github.com/jacobzhaoziyuan/MT-UDA

공식 코드

MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels

https://github.com/jacobzhaoziyuan/SAAL

공식 코드

Self-supervised Assisted Active Learning for Skin Lesion Segmentation

https://github.com/jacobzhaoziyuan/le-uda

공식 코드

LE-UDA: Label-efficient unsupervised domain adaptation for medical image segmentation

https://github.com/jalilnkh/kartalol-nir-isl2021031301

공식 코드

KartalOl: Transfer learning using deep neural network for iris segmentation and localization: New dataset for iris segmentation

https://github.com/james128333/HarDNet-MSEG

공식 코드

HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS

https://github.com/janelia-flyem/DVIDSparkServices

공식 코드

Large-Scale Electron Microscopy Image Segmentation in Spark

https://github.com/jayparanjape/blackbox

공식 코드

Blackbox Adaptation for Medical Image Segmentation

https://github.com/jayparanjape/svdsam

공식 코드

S-SAM: SVD-based Fine-Tuning of Segment Anything Model for Medical Image Segmentation

https://github.com/jcruan519/ege-unet

공식 코드

EGE-UNet: an Efficient Group Enhanced UNet for skin lesion segmentation

https://github.com/jcruan519/malunet

공식 코드

MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentation

https://github.com/jcruan519/mew-unet

공식 코드

MEW-UNet: Multi-axis representation learning in frequency domain for medical image segmentation

https://github.com/jcruan519/vm-unet

공식 코드

VM-UNet: Vision Mamba UNet for Medical Image Segmentation

https://github.com/jcwang123/BA-Transformer

공식 코드

Boundary-aware Transformers for Skin Lesion Segmentation

https://github.com/jcwang123/fedlc

공식 코드

Personalizing Federated Medical Image Segmentation via Local Calibration

https://github.com/jcwang123/xboundformer

공식 코드

XBound-Former: Toward Cross-scale Boundary Modeling in Transformers

https://github.com/jeming-creater/hwa-unetr

공식 코드

HWA-UNETR: Hierarchical Window Aggregate UNETR for 3D Multimodal Gastric Lesion Segmentation

https://github.com/jeremy-chopin/apacosi

공식 코드

Model-based inexact graph matching on top of CNNs for semantic scene understanding

https://github.com/jeroenbertels/dicegrad

공식 코드

The Dice loss in the context of missing or empty labels: Introducing $Φ$ and $ε$

https://github.com/jeya-maria-jose/KiU-Net-pytorch

공식 코드

KiU-Net: Towards Accurate Segmentation of Biomedical Images using Over-complete Representations

https://github.com/jeya-maria-jose/Medical-Transformer

공식 코드

Medical Transformer: Gated Axial-Attention for Medical Image Segmentation

https://github.com/jeya-maria-jose/on-the-fly-adaptation

공식 코드

On-the-Fly Test-time Adaptation for Medical Image Segmentation

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