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/timothy102/Brain-FMRI

공식 코드

AGD-Autoencoder: Attention Gated Deep Convolutional Autoencoder for Brain Tumor Segmentation

https://github.com/tingliu/glia

공식 코드

SSHMT: Semi-supervised Hierarchical Merge Tree for Electron Microscopy Image Segmentation

https://github.com/tinymilky/NeRD

공식 코드

NeRD: Neural Representation of Distribution for Medical Image Segmentation

https://github.com/tinymilky/RSANet

공식 코드

RSANet: Recurrent Slice-wise Attention Network for Multiple Sclerosis Lesion Segmentation

https://github.com/tinymilky/deda

공식 코드

DAGrid: Directed Accumulator Grid

https://github.com/tio-ikim/cellvit

공식 코드

CellViT: Vision Transformers for Precise Cell Segmentation and Classification

https://github.com/tio-ikim/cellvit-plus-plus

공식 코드

CellViT++: Energy-Efficient and Adaptive Cell Segmentation and Classification Using Foundation Models

https://github.com/tissueimageanalytics/conic

공식 코드

CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

https://github.com/tljxyys/perspective-unet

공식 코드

Perspective+ Unet: Enhancing Segmentation with Bi-Path Fusion and Efficient Non-Local Attention for Superior Receptive Fields

https://github.com/tntek/dspnet

공식 코드

Few-Shot Medical Image Segmentation with High-Fidelity Prototypes

https://github.com/tomeramit/annotator-consensus-prediction

공식 코드

Annotator Consensus Prediction for Medical Image Segmentation with Diffusion Models

https://github.com/topoxlab/topointeraction

공식 코드

Learning Topological Interactions for Multi-Class Medical Image Segmentation

https://github.com/torpedo2648/pclmix

공식 코드

PCLMix: Weakly Supervised Medical Image Segmentation via Pixel-Level Contrastive Learning and Dynamic Mix Augmentation

https://github.com/tpnam0901/QTSeg

공식 코드

QTSeg: A Query Token-Based Architecture for Efficient 2D Medical Image Segmentation

https://github.com/tpopordanoska/calibration_and_bias

공식 코드

On the relationship between calibrated predictors and unbiased volume estimation

https://github.com/tranngocduvnvp/litenext

공식 코드

LiteNeXt: A Novel Lightweight ConvMixer-based Model with Self-embedding Representation Parallel for Medical Image Segmentation

https://github.com/trustworthy-ai-uu-nki/cross-modal-segmentation

공식 코드

Enhancing Cross-Modal Medical Image Segmentation through Compositionality

https://github.com/trustworthy-ai-uu-nki/federated-learning-disentanglement

공식 코드

FedGS: Federated Gradient Scaling for Heterogeneous Medical Image Segmentation

https://github.com/tuat-novice/maprotonet

공식 코드

MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part Network for 3D Magnetic Resonance Imaging Brain Tumor Classification

https://github.com/tum-aimed/octa-seg

공식 코드

Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations

https://github.com/tushaarkataria/histopathology-domain-specific-pretraining

공식 코드

To pretrain or not to pretrain? A case study of domain-specific pretraining for semantic segmentation in histopathology

https://github.com/tvseg/dmoe

공식 코드

Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective

https://github.com/twni2016/EBP

공식 코드

Elastic Boundary Projection for 3D Medical Image Segmentation

https://github.com/twni2016/Elastic-Boundary-Projection

공식 코드

Elastic Boundary Projection for 3D Medical Image Segmentation

https://github.com/uark-aicv/meganet

공식 코드

MEGANet: Multi-Scale Edge-Guided Attention Network for Weak Boundary Polyp Segmentation

https://github.com/uceclz0/DEFU-Net

공식 코드

Dual Encoder Fusion U-Net (DEFU-Net) for Cross-manufacturer Chest X-ray Segmentation

https://github.com/uci-cbcl/RP-Net

공식 코드

Recurrent Mask Refinement for Few-Shot Medical Image Segmentation

https://github.com/ucsc-vlaa/swinmm

공식 코드

SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation

https://github.com/ucwxb/i-medsam

공식 코드

I-MedSAM: Implicit Medical Image Segmentation with Segment Anything

https://github.com/ugurdemir/tranformer_liver_segmentation

공식 코드

Transformer based Generative Adversarial Network for Liver Segmentation

https://github.com/uhlmanngroup/AURA-Net

공식 코드

aura-net : robust segmentation of phase-contrast microscopy images with few annotations

https://github.com/ukaukaaaa/gazesam

공식 코드

GazeSAM: What You See is What You Segment

https://github.com/um2ii/sam_dataannotation

공식 코드

Anytime, Anywhere, Anyone: Investigating the Feasibility of Segment Anything Model for Crowd-Sourcing Medical Image Annotations

https://github.com/uncbiag/isegformer

공식 코드

iSegFormer: Interactive Segmentation via Transformers with Application to 3D Knee MR Images

https://github.com/uncertaintyquantification/cell_segmentation

공식 코드

Unsupervised cell segmentation by fast Gaussian Processes

https://github.com/undercutspiky/sfl

공식 코드

Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology

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