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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 411–420 of 436 papers

TitleStatusHype
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
MedMAP: Promoting Incomplete Multi-modal Brain Tumor Segmentation with Alignment—0
Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation—0
Memory Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain Tumor Segmentation—0
ME-Net: Multi-Encoder Net Framework for Brain Tumor Segmentation—0
Mind the Gap: Promoting Missing Modality Brain Tumor Segmentation with Alignment—0
Modality-Aware and Shift Mixer for Multi-modal Brain Tumor Segmentation—0
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