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Brain Tumor Segmentation

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.

( Image credit: Brain Tumor Segmentation with Deep Neural Networks )

Papers

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Showing 71–80 of 436 papers

TitleStatusHype
Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion modelsCode1
CANet: Context Aware Network for 3D Brain Glioma SegmentationCode1
Generalized Wasserstein Dice Loss, Test-time Augmentation, and Transformers for the BraTS 2021 challengeCode1
Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for brain tumor segmentation: BraTS 2020 challengeCode1
Brain Tumor Segmentation with Deep Neural NetworksCode1
Deep Learning Based Brain Tumor Segmentation: A SurveyCode1
KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric SegmentationCode1
Knowledge Distillation for Brain Tumor SegmentationCode1
A Robust Volumetric Transformer for Accurate 3D Tumor SegmentationCode1
Fed-MUnet: Multi-modal Federated Unet for Brain Tumor SegmentationCode1
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