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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/reeive/rehydil

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Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing Modalities

https://github.com/renato145/posthocOS

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Post-hoc Overall Survival Time Prediction from Brain MRI

https://github.com/rezazad68/smunet

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SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities

https://github.com/rixez/brats21_kaist_mri_lab

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Extending nn-UNet for brain tumor segmentation

https://github.com/rsinghlab/gnn-tumor-seg

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A Joint Graph and Image Convolution Network for Automatic Brain Tumor Segmentation

https://github.com/scut-cszcl/sfusion

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SFusion: Self-attention based N-to-One Multimodal Fusion Block

https://github.com/sergiormpereira/rr_segse

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Adaptive feature recombination and recalibration for semantic segmentation: application to brain tumor segmentation in MRI

https://github.com/shu-hai/two-stage-VAE-Attention-gate-BraTS2020

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A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation

https://github.com/sinAshish/Multi-Scale-Attention

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Multi-scale self-guided attention for medical image segmentation

https://github.com/sotiraslab/QCResUNet

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QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality Prediction

https://github.com/sotiraslab/dlk

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D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image Segmentation

https://github.com/soumbane/dualselfdistillation

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Volumetric medical image segmentation through dual self-distillation in U-shaped networks

https://github.com/sveinnpalsson/semivaebrats

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Semi-Supervised Variational Autoencoder for Survival Prediction

https://github.com/tianzetang0504/ukan-ep

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UKAN-EP: Enhancing U-KAN with Efficient Attention and Pyramid Aggregation for 3D Multi-Modal MRI Brain Tumor Segmentation

https://github.com/timothy102/Brain-FMRI

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

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

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

https://github.com/vinyehshaw/hff

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Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective

https://github.com/vpulab/med-sam-brain

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GBT-SAM: Adapting a Foundational Deep Learning Model for Generalizable Brain Tumor Segmentation via Efficient Integration of Multi-Parametric MRI Data

https://github.com/wendywaaaaang/a4-unet

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A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation

https://github.com/wetothemoon/braintumorsegmentation

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Brain Tumor Segmentation Based on Deep Learning, Attention Mechanisms, and Energy-Based Uncertainty Prediction

https://github.com/willbeanyang/swin-der

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Optimizing Medical Image Segmentation with Advanced Decoder Design

https://github.com/windstormer/Cfd-CAM

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A Novel Confidence Induced Class Activation Mapping for MRI Brain Tumor Segmentation

https://github.com/windstormer/ame-cam

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AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain Tumor

https://github.com/woodywff/brats_2019

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3D U-Net Based Brain Tumor Segmentation and Survival Days Prediction

https://github.com/woodywff/nas_3d_unet

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Neural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance Imaging

https://github.com/wujunde/medsegdiff

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MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model

https://github.com/xrt11/tumor-segmentation

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A Weakly Supervised and Globally Explainable Learning Framework for Brain Tumor Segmentation

https://github.com/xyx1024/SimTxtSeg

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SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text Cues

https://github.com/yaozhang93/mmformer

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mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation

https://github.com/yaq007/Autofocus-Layer

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Autofocus Layer for Semantic Segmentation

https://github.com/yaziciz/GLIMS

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Attention-Enhanced Hybrid Feature Aggregation Network for 3D Brain Tumor Segmentation

https://github.com/ysmei97/multimodal_pci_mask

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Exploiting Partial Common Information Microstructure for Multi-Modal Brain Tumor Segmentation

https://github.com/yyliu01/tracoco

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Translation Consistent Semi-supervised Segmentation for 3D Medical Images

https://github.com/zhenxuan-zhang/tucl_brainseg

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Task-oriented Uncertainty Collaborative Learning for Label-Efficient Brain Tumor Segmentation

https://github.com/zhongaobo/asymmetry-bts

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Enhancing Incomplete Multi-modal Brain Tumor Segmentation with Intra-modal Asymmetry and Inter-modal Dependency

https://github.com/zinic95/HFTrans

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Hybrid-Fusion Transformer for Multisequence MRI

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