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/levayz/protosam

공식 코드

ProtoSAM: One-Shot Medical Image Segmentation With Foundational Models

https://github.com/lhaof/asa

공식 코드

Attentive Symmetric Autoencoder for Brain MRI Segmentation

https://github.com/lhaof/asc

공식 코드

ASC: Appearance and Structure Consistency for Unsupervised Domain Adaptation in Fetal Brain MRI Segmentation

https://github.com/lhaof/cellseg

공식 코드

Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images

https://github.com/lhaof/nnmamba

공식 코드

nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Model

https://github.com/liamchalcroft/contrast-squared

공식 코드

Unified 3D MRI Representations via Sequence-Invariant Contrastive Learning

https://github.com/liamchalcroft/mdunet

공식 코드

Large-kernel Attention for Efficient and Robust Brain Lesion Segmentation

https://github.com/liamchalcroft/qsynth

공식 코드

Domain-Agnostic Stroke Lesion Segmentation Using Physics-Constrained Synthetic Data

https://github.com/liamchalcroft/synthstroke

공식 코드

Synthetic Data for Robust Stroke Segmentation

https://github.com/liamchalcroft/synthstrokespm

공식 코드

Synthetic Data for Robust Stroke Segmentation

https://github.com/liamheng/non-iid_medical_image_segmentation

공식 코드

Frequency-mixed Single-source Domain Generalization for Medical Image Segmentation

https://github.com/lich0031/AIDE

공식 코드

Annotation-efficient deep learning for automatic medical image segmentation

https://github.com/lihaoliu-cambridge/simultaneous_semantic_and_instance_segmentation

공식 코드

Simultaneous Semantic and Instance Segmentation for Colon Nuclei Identification and Counting

https://github.com/lilygoli/longitudinalCOVID

공식 코드

Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs

https://github.com/limingxing00/retinal-vessel-segmentation-isbi2022

공식 코드

Retinal Vessel Segmentation with Pixel-wise Adaptive Filters

https://github.com/lin-tianyu/stable-diffusion-seg

공식 코드

Stable Diffusion Segmentation for Biomedical Images with Single-step Reverse Process

https://github.com/lingchm/medassist-liver-cancer

공식 코드

SmoothSegNet: A Global-Local Framework for Liver Tumor Segmentation with Clinical KnowledgeInformed Label Smoothing

https://github.com/lingrayy/plpb

공식 코드

Robust Source-Free Domain Adaptation for Fundus Image Segmentation

https://github.com/linxuanhan/e-mim

공식 코드

Enhanced Masked Image Modeling to Avoid Model Collapse on Multi-modal MRI Datasets

https://github.com/linxuanhan/m2-mae

공식 코드

Enhanced Masked Image Modeling to Avoid Model Collapse on Multi-modal MRI Datasets

https://github.com/linzy0227/pdminet

공식 코드

Prototype-Driven and Multi-Expert Integrated Multi-Modal MR Brain Tumor Image Segmentation

https://github.com/lironui/MACU-Net

공식 코드

MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images

https://github.com/lironui/U-Net-with-Multi-Scale-Skip-Connections-and-Asymmetric-Convolution-Blocks

공식 코드

MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images

https://github.com/liu-yuxi/fedfms

공식 코드

FedFMS: Exploring Federated Foundation Models for Medical Image Segmentation

https://github.com/liu37972/Fuzzy-Seg-Deep-DuS-KFCM-

공식 코드

Enhancing Diagnostic Precision in Gastric Bleeding through Automated Lesion Segmentation: A Deep DuS-KFCM Approach

https://github.com/liuquande/FedDG-ELCFS

공식 코드

FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space

https://github.com/liuquande/SAML

공식 코드

Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains

https://github.com/liuzhuang13/DenseNet

공식 코드

Densely Connected Convolutional Networks

https://github.com/liyihao76/MMAC_LaTIM_Solution

공식 코드

Automated Detection of Myopic Maculopathy in MMAC 2023: Achievements in Classification, Segmentation, and Spherical Equivalent Prediction

https://github.com/ljollans/trunet

공식 코드

Vision Transformers increase efficiency of 3D cardiac CT multi-label segmentation

https://github.com/lkpengcs/dcda

공식 코드

Unsupervised Domain Adaptation for Cross-Modality Retinal Vessel Segmentation via Disentangling Representation Style Transfer and Collaborative Consistency Learning

https://github.com/lkpengcs/rvms

공식 코드

Student Becomes Decathlon Master in Retinal Vessel Segmentation via Dual-teacher Multi-target Domain Adaptation

https://github.com/lkshrsch/multiaxial_brain_segmenter

공식 코드

Full-Head Segmentation of MRI with Abnormal Brain Anatomy: Model and Data Release

https://github.com/llmir/fedicra

공식 코드

Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation

https://github.com/llmir/fedlppa

공식 코드

FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentation

https://github.com/lmiapc/fednnu-net

공식 코드

FedFMS: Exploring Federated Foundation Models for Medical Image Segmentation

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