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/nkicsl/sf-unet

공식 코드

Spatial-Frequency Dual Progressive Attention Network For Medical Image Segmentation

https://github.com/nkuhealong/pmcnet

공식 코드

Progressive Multi-scale Consistent Network for Multi-class Fundus Lesion Segmentation

https://github.com/nobodyplayer1/vm-unetv2

공식 코드

VM-UNET-V2 Rethinking Vision Mamba UNet for Medical Image Segmentation

https://github.com/notmatthancock/level-set-machine-learning

공식 코드

Level set image segmentation with velocity term learned from data with applications to lung nodule segmentation

https://github.com/noureddinekhiati/semi-supervised-lung-segmentation

공식 코드

Shape-aware synthesis of pathological lung CT scans using CycleGAN for enhanced semi-supervised lung segmentation

https://github.com/novicefodder/os-medseg

공식 코드

Distillation Learning Guided by Image Reconstruction for One-Shot Medical Image Segmentation

https://github.com/nqnguyen812/mambau-lite

공식 코드

MambaU-Lite: A Lightweight Model based on Mamba and Integrated Channel-Spatial Attention for Skin Lesion Segmentation

https://github.com/nubagcilab/diffboost

공식 코드

DiffBoost: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model

https://github.com/nubagcilab/pansegnet

공식 코드

Large-Scale Multi-Center CT and MRI Segmentation of Pancreas with Deep Learning

https://github.com/nubagcilab/pediatric-brain-tumor-segmentation-model

공식 코드

A New Logic For Pediatric Brain Tumor Segmentation

https://github.com/nubagcilab/peri-pancreatic-edema-detection

공식 코드

Detection of Peri-Pancreatic Edema using Deep Learning and Radiomics Techniques

https://github.com/numisveinsson/SeqSeg

공식 코드

SeqSeg: Learning Local Segments for Automatic Vascular Model Construction

https://github.com/nus-tim/dems

공식 코드

Segmenting Medical Images with Limited Data

https://github.com/nyukat/GMIC

공식 코드

An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization

https://github.com/oliverjm1/bmvc_menisc_seg

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Putting the Segment Anything Model to the Test with 3D Knee MRI - A Comparison with State-of-the-Art Performance

https://github.com/omfsdigital/cyto-r-cnn

공식 코드

Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images

https://github.com/omicsml/dance

공식 코드

Deep Learning in Single-Cell Analysis

https://github.com/omid-nejati/befunet

공식 코드

BEFUnet: A Hybrid CNN-Transformer Architecture for Precise Medical Image Segmentation

https://github.com/onurboyar/Retinal-Vessel-Segmentation

공식 코드

Exploring The Limits Of Data Augmentation For Retinal Vessel Segmentation

https://github.com/openmedlab/mis-fm

공식 코드

MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset

https://github.com/orange-czh/mamba-sea

공식 코드

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation

https://github.com/orobix/retina-unet/blob/master/lib/pre_processing.py

공식 코드

Enhancing Retinal Vascular Structure Segmentation in Images With a Novel Design Two-Path Interactive Fusion Module Model

https://github.com/ortonwang/pcdal

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PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification

https://github.com/ortonwang/plgdf

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Pseudo Label-Guided Data Fusion and Output Consistency for Semi-Supervised Medical Image Segmentation

https://github.com/ortonwang/scribblevs

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ScribbleVS: Scribble-Supervised Medical Image Segmentation via Dynamic Competitive Pseudo Label Selection

https://github.com/osupcvlab/mobileunetr

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MobileUNETR: A Lightweight End-To-End Hybrid Vision Transformer For Efficient Medical Image Segmentation

https://github.com/osupcvlab/segformer3d

공식 코드

SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation

https://github.com/othmanela/medical_cert_seg

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Certification of Deep Learning Models for Medical Image Segmentation

https://github.com/oulu-imeds/log-vmamba

공식 코드

LoG-VMamba: Local-Global Vision Mamba for Medical Image Segmentation

https://github.com/oulu-imeds/sauna

공식 코드

Image-level Regression for Uncertainty-aware Retinal Image Segmentation

https://github.com/oulu-imeds/singr

공식 코드

SiNGR: Brain Tumor Segmentation via Signed Normalized Geodesic Transform Regression

https://github.com/ounissimehdi/phagostat

공식 코드

PhagoStat a scalable and interpretable end to end framework for efficient quantification of cell phagocytosis in neurodegenerative disease studies

https://github.com/ouyangjiahong/representation-disentanglement

공식 코드

Representation Disentanglement for Multi-modal brain MR Analysis

https://github.com/ozan-oktay/Attention-Gated-Networks

공식 코드

Attention U-Net: Learning Where to Look for the Pancreas

https://github.com/ozguncck/recovering-the-imperfect

공식 코드

Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteins

https://github.com/pancakewaffles/metasrgan-tutorial

공식 코드

Arbitrary Scale Super-Resolution for Brain MRI Images

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