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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 401–410 of 436 papers

TitleStatusHype
Investigating certain choices of CNN configurations for brain lesion segmentation—0
Joint brain tumor segmentation from multi MR sequences through a deep convolutional neural network—0
KMD: Koopman Multi-modality Decomposition for Generalized Brain Tumor Segmentation under Incomplete Modalities—0
Knowledge distillation from multi-modal to mono-modal segmentation networks—0
Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities—0
LATUP-Net: A Lightweight 3D Attention U-Net with Parallel Convolutions for Brain Tumor Segmentation—0
Learning Data Augmentation for Brain Tumor Segmentation with Coarse-to-Fine Generative Adversarial Networks—0
Learning Multi-Modal Brain Tumor Segmentation from Privileged Semi-Paired MRI Images with Curriculum Disentanglement Learning—0
Learning to Learn Unlearned Feature for Brain Tumor Segmentation—0
Leveraging SeNet and ResNet Synergy within an Encoder-Decoder Architecture for Glioma Detection—0
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