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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 331–340 of 436 papers

TitleStatusHype
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
MAG-Net: Multi-task attention guided network for brain tumor segmentation and classification—0
Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation—0
Medical Image Analysis using Deep Relational Learning—0
Medical Transformer: Universal Brain Encoder for 3D MRI Analysis—0
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