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/siat-ct-lab/swdl

공식 코드

SWDL: Stratum-Wise Difference Learning with Deep Laplacian Pyramid for Semi-Supervised 3D Intracranial Hemorrhage Segmentation

https://github.com/sichengs/manet

공식 코드

Manifold-Aware Local Feature Modeling for Semi-Supervised Medical Image Segmentation

https://github.com/simongraham/dsf-cnn

공식 코드

Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images

https://github.com/simonustc/mcpa-for-2d-medical-image-segmentation

공식 코드

MCPA: Multi-scale Cross Perceptron Attention Network for 2D Medical Image Segmentation

https://github.com/simonzeng7108/video-swinunet

공식 코드

Video-SwinUNet: Spatio-temporal Deep Learning Framework for VFSS Instance Segmentation

https://github.com/simonzhou86/en_dran

공식 코드

Edge-Enhanced Dilated Residual Attention Network for Multimodal Medical Image Fusion

https://github.com/simzhangbest/hmt-unet

공식 코드

HMT-UNet: A hybird Mamba-Transformer Vision UNet for Medical Image Segmentation

https://github.com/simzhangbest/sam2path

공식 코드

Path-SAM2: Transfer SAM2 for digital pathology semantic segmentation

https://github.com/sinAshish/Multi-Scale-Attention

공식 코드

Multi-scale self-guided attention for medical image segmentation

https://github.com/siyi-wind/avit

공식 코드

AViT: Adapting Vision Transformers for Small Skin Lesion Segmentation Datasets

https://github.com/siyi-wind/mdvit

공식 코드

MDViT: Multi-domain Vision Transformer for Small Medical Image Segmentation Datasets

https://github.com/sjtubme-qianlab/dual-task-seg

공식 코드

A Dual-Task Synergy-Driven Generalization Framework for Pancreatic Cancer Segmentation in CT Scans

https://github.com/sldgroup/emcad

공식 코드

EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation

https://github.com/smkamrulhasan/mt-ctl

공식 코드

A Multi-Task Cross-Task Learning Architecture for Ad-hoc Uncertainty Estimation in 3D Cardiac MRI Image Segmentation

https://github.com/smkamrulhasan/mtctl

공식 코드

A Multi-Task Cross-Task Learning Architecture for Ad-hoc Uncertainty Estimation in 3D Cardiac MRI Image Segmentation

https://github.com/snavalm/mood22

공식 코드

Achieving state-of-the-art performance in the Medical Out-of-Distribution (MOOD) challenge using plausible synthetic anomalies

https://github.com/sohambasu07/dr_2021

공식 코드

Segmentation of Blood Vessels, Optic Disc Localization, Detection of Exudates and Diabetic Retinopathy Diagnosis from Digital Fundus Images

https://github.com/songchangwei/3dct-sd-ivh-ich

공식 코드

HemSeg-200: A Voxel-Annotated Dataset for Intracerebral Hemorrhages Segmentation in Brain CT Scans

https://github.com/sophieostmeier/uncertainsmallempty

공식 코드

USE-Evaluator: Performance Metrics for Medical Image Segmentation Models with Uncertain, Small or Empty Reference Annotations

https://github.com/sotiraslab/AgileFormer

공식 코드

AgileFormer: Spatially Agile Transformer UNet for Medical Image Segmentation

https://github.com/sotiraslab/QCResUNet

공식 코드

QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality Prediction

https://github.com/sotiraslab/dlk

공식 코드

D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image Segmentation

https://github.com/soumbane/dualselfdistillation

공식 코드

Volumetric medical image segmentation through dual self-distillation in U-shaped networks

https://github.com/soumickmj/turbolift

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Liver Segmentation using Turbolift Learning for CT and Cone-beam C-arm Perfusion Imaging

https://github.com/splinterli/sattca

공식 코드

Scale-aware Test-time Click Adaptation for Pulmonary Nodule and Mass Segmentation

https://github.com/sqbqamar/SAMA-UNet

공식 코드

SAMA-UNet: Enhancing Medical Image Segmentation with Self-Adaptive Mamba-Like Attention and Causal-Resonance Learning

https://github.com/sqbqamar/scalefusionnet

공식 코드

ScaleFusionNet: Transformer-Guided Multi-Scale Feature Fusion for Skin Lesion Segmentation

https://github.com/sr0920/cit-net

공식 코드

CiT-Net: Convolutional Neural Networks Hand in Hand with Vision Transformers for Medical Image Segmentation

https://github.com/sr0920/tec-net

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TEC-Net: Vision Transformer Embrace Convolutional Neural Networks for Medical Image Segmentation

https://github.com/srijay-lab/synclay-framework

공식 코드

SynCLay: Interactive Synthesis of Histology Images from Bespoke Cellular Layouts

https://github.com/stanfordmlgroup/CheXseg

공식 코드

CheXseg: Combining Expert Annotations with DNN-generated Saliency Maps for X-ray Segmentation

https://github.com/stanfordmlgroup/auto-generate-wls

공식 코드

Auto-Generating Weak Labels for Real & Synthetic Data to Improve Label-Scarce Medical Image Segmentation

https://github.com/starbucks-drinker/mi-segnet

공식 코드

MI-SegNet: Mutual Information-Based US Segmentation for Unseen Domain Generalization

https://github.com/stardist/stardist

공식 코드

Cell Detection with Star-convex Polygons

https://github.com/stegmaierj/CellShapeAnalysis

공식 코드

Semi-Automatic Generation of Tight Binary Masks and Non-Convex Isosurfaces for Quantitative Analysis of 3D Biological Samples

https://github.com/stegmaierj/Cellpose3D

공식 코드

Robust 3D Cell Segmentation: Extending the View of Cellpose

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