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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 201–225 of 436 papers

TitleStatusHype
Robustness of Brain Tumor Segmentation—0
Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation—0
Segment Anything Model for Brain Tumor Segmentation—0
Segmentation of brain tumor on magnetic resonance imaging using a convolutional architecture—0
Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Network—0
Segmentation of Pediatric Brain Tumors using a Radiologically informed, Deep Learning Cascade—0
Segmenting Brain Tumors with Symmetry—0
Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging—0
Self-calibrated convolution towards glioma segmentation—0
Self-semantic contour adaptation for cross modality brain tumor segmentation—0
Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik's Cube—0
Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout—0
Self-supervised Tumor Segmentation through Layer Decomposition—0
Sequential 3D U-Nets for Biologically-Informed Brain Tumor Segmentation—0
SoftSeg: Advantages of soft versus binary training for image segmentation—0
Source Identification: A Self-Supervision Task for Dense Prediction—0
Spherical coordinates transformation pre-processing in Deep Convolution Neural Networks for brain tumor segmentation in MRI—0
Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation—0
Squeeze Excitation Embedded Attention UNet for Brain Tumor Segmentation—0
Stratify or Inject: Two Simple Training Strategies to Improve Brain Tumor Segmentation—0
Deep Superpixel Generation and Clustering for Weakly Supervised Segmentation of Brain Tumors in MR Images—0
Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology—0
SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation—0
Synthesizing Missing MRI Sequences from Available Modalities using Generative Adversarial Networks in BraTS Dataset—0
Synthetic Poisoning Attacks: The Impact of Poisoned MRI Image on U-Net Brain Tumor Segmentation—0
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