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Brain Tumor Segmentation

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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/jupitern52/textbrats

공식 코드

TextBraTS: Text-Guided Volumetric Brain Tumor Segmentation with Innovative Dataset Development and Fusion Module Exploration

https://github.com/justatinydot/bitr-unet

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BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor Segmentation

https://github.com/kk42yy/demoseg

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Decoupling Feature Representations of Ego and Other Modalities for Incomplete Multi-modal Brain Tumor Segmentation

https://github.com/koriavinash1/BioExp

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Demystifying Brain Tumour Segmentation Networks: Interpretability and Uncertainty Analysis

https://github.com/lab-smile/brainsegfounder

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BrainSegFounder: Towards 3D Foundation Models for Neuroimage Segmentation

https://github.com/lab-smile/gatorbrain

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BrainSegFounder: Towards 3D Foundation Models for Neuroimage Segmentation

https://github.com/lachinov/brats2019

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Knowledge Distillation for Brain Tumor Segmentation

https://github.com/lescientifik/open_brats2020

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Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution

https://github.com/linzy0227/pdminet

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

https://github.com/louisdo2108/acs-nnu-net

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Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal Embedding

https://github.com/lucasfidon/trabit_brats2021

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Generalized Wasserstein Dice Loss, Test-time Augmentation, and Transformers for the BraTS 2021 challenge

https://github.com/majid9418/eresu-net

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Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learning

https://github.com/maloadba/mgenseg_2d

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M-GenSeg: Domain Adaptation For Target Modality Tumor Segmentation With Annotation-Efficient Supervision

https://github.com/mateuszbuda/brain-segmentation

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Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm

https://github.com/mathildefaanes/us_brain_tumor_segmentation

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Automatic brain tumor segmentation in 2D intra-operative ultrasound images using MRI tumor annotations

https://github.com/mdciri/3D-augmentation-techniques

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What is the best data augmentation for 3D brain tumor segmentation?

https://github.com/mdciri/augmentation

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What is the best data augmentation for 3D brain tumor segmentation?

https://github.com/mkang315/mctseg

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A Multimodal Feature Distillation with CNN-Transformer Network for Brain Tumor Segmentation with Incomplete Modalities

https://github.com/mobarakol/3D_Attention_UNet

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Brain Tumor Segmentation and Survival Prediction using 3D Attention UNet

https://github.com/muhamadusman/assist

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Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion models

https://github.com/nasim-aust/braintumor-segmentation-3dunet-brats

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Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysis

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

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A New Logic For Pediatric Brain Tumor Segmentation

https://github.com/osupcvlab/segformer3d

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SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation

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

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SiNGR: Brain Tumor Segmentation via Signed Normalized Geodesic Transform Regression

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

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Representation Disentanglement for Multi-modal brain MR Analysis

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

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

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

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Arbitrary Scale Super-Resolution for Brain MRI Images

https://github.com/pashtari/factorizer

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Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image Segmentation

https://github.com/pfriedri/cwdm

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cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image Synthesis

https://github.com/project-monai/research-contributions

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UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image Segmentation

https://github.com/python-arch/biombz-optimizing-brain-tumor-segmentation-with-mednext-brats-2024-ssa-and-pediatrics

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Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and Pediatrics

https://github.com/qdaiing/fedmema

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Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentation

https://github.com/quanato607/xlstm-hved

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XLSTM-HVED: Cross-Modal Brain Tumor Segmentation and MRI Reconstruction Method Using Vision XLSTM and Heteromodal Variational Encoder-Decoder

https://github.com/ragmeh11/qu-brats

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QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

https://github.com/raun1/ASC-NET

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ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly Segmentation

https://github.com/razeineldin/DeepSeg

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DeepSeg: Deep Neural Network Framework for Automatic Brain Tumor Segmentation using Magnetic Resonance FLAIR Images

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