Papers with Code 아카이브

Brain Tumor Segmentation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 12
보존된 평가 기록: 27

Brain Tumor Segmentation is a medical image analysis task that involves the separation of brain tumors from normal brain tissue in magnetic resonance imaging (MRI) scans. The goal of brain tumor segmentation is to produce a binary or multi-class segmentation map that accurately reflects the location and extent of the tumor.

<span style="color:grey; opacity: 0.6">( Image credit: Brain Tumor Segmentation with Deep Neural Networks )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/askerlee/segtran

공식 코드

Medical Image Segmentation Using Squeeze-and-Expansion Transformers

https://github.com/auroua/hrstnet

공식 코드

High-Resolution Swin Transformer for Automatic Medical Image Segmentation

https://github.com/bidur-khanal/mvaal-medical-images

공식 코드

M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis Tasks

https://github.com/billhhh/MCKD

공식 코드

Meta-Learned Modality-Weighted Knowledge Distillation for Robust Multi-Modal Learning with Missing Data

https://github.com/biomedia-mbzuai/brats2024_biomediambz

공식 코드

On Enhancing Brain Tumor Segmentation Across Diverse Populations with Convolutional Neural Networks

https://github.com/boqian333/e2enet-medical

공식 코드

E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation

https://github.com/brainlesion/brats

공식 코드

BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis

https://github.com/brats-inpainting/2023_challenge

공식 코드

The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via Inpainting

https://github.com/camera-mri/spark2024

공식 코드

Parameter-efficient Fine-tuning for improved Convolutional Baseline for Brain Tumor Segmentation in Sub-Saharan Africa Adult Glioma Dataset

https://github.com/ccarliu/m3ae

공식 코드

M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalities

https://github.com/cchen-cc/Robust-Mseg

공식 코드

Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion

https://github.com/chenhong-zhou/OM-Net

공식 코드

One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor Segmentation

https://github.com/chenzhao2023/upmad_net_brainseg

공식 코드

UPMAD-Net: A Brain Tumor Segmentation Network with Uncertainty Guidance and Adaptive Multimodal Feature Fusion

https://github.com/chollette/SEDNet_Shallow-Encoder-Decoder-Network-for-Brain-Tumor-Segmentation

공식 코드

SEDNet: Shallow Encoder-Decoder Network for Brain Tumor Segmentation

https://github.com/clinical-and-translational-imaging-lab/brats-e1d3

공식 코드

E1D3 U-Net for Brain Tumor Segmentation: Submission to the RSNA-ASNR-MICCAI BraTS 2021 Challenge

https://github.com/cocofeat/tbrats

공식 코드

TBraTS: Trusted Brain Tumor Segmentation

https://github.com/cucl-2/dc-seg

공식 코드

DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing Modalities

https://github.com/cvlab-stonybrook/SelfMedMAE

공식 코드

Self Pre-training with Masked Autoencoders for Medical Image Classification and Segmentation

https://github.com/delugewalker/CGA-U-Net

공식 코드

Category Guided Attention Network for Brain Tumor Segmentation in MRI

https://github.com/eceisik/FeTS_Challenge_METU_FL_Team

공식 코드

Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor Segmentation

https://github.com/fjr9516/da_nnunet

공식 코드

Unsupervised Domain Adaptation for Pediatric Brain Tumor Segmentation

https://github.com/gama-ufsc/brain-extraction-for-tumor-segmentation

공식 코드

Towards fully automated deep-learning-based brain tumor segmentation: is brain extraction still necessary?

https://github.com/haail/fins-for-biomedical-imaging

공식 코드

Feature Imitating Networks Enhance The Performance, Reliability And Speed Of Deep Learning On Biomedical Image Processing Tasks

https://github.com/hamghalam/MGP-VAE

공식 코드

Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma Segmentation

https://github.com/hiest/distmis

공식 코드

Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation

https://github.com/hilab-git/pa-seg

공식 코드

PA-Seg: Learning from Point Annotations for 3D Medical Image Segmentation using Contextual Regularization and Cross Knowledge Distillation

https://github.com/himashi92/vizviva_brats_2021

공식 코드

Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task

https://github.com/himashi92/vizviva_fets_2022

공식 코드

Hybrid Window Attention Based Transformer Architecture for Brain Tumor Segmentation

https://github.com/himashi92/vt-unet

공식 코드

A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation

https://github.com/huaibei-normal-university-cv-laboratory/mbdresunet

공식 코드

MBDRes-U-Net: Multi-Scale Lightweight Brain Tumor Segmentation Network

https://github.com/i-sahajmistry/nnunet_brats2023

공식 코드

Multiscale Encoder and Omni-Dimensional Dynamic Convolution Enrichment in nnU-Net for Brain Tumor Segmentation

https://github.com/imatge-upc/mri-braintumor-segmentation

공식 코드

MRI brain tumor segmentation and uncertainty estimation using 3D-UNet architectures

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

공식 코드

KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation

https://github.com/jordan-colman/DR-Unet104

공식 코드

DR-Unet104 for Multimodal MRI brain tumor segmentation

https://github.com/juliawolleb/diffusion-based-segmentation

공식 코드

Diffusion Models for Implicit Image Segmentation Ensembles

https://github.com/juntongkuki/pytorch-dsfnet

공식 코드

DSFNet: Dual-GCN and Location-fused Self-attention with Weighted Fast Normalized Fusion for Polyps Segmentation

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