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Tumor Segmentation

Tumor Segmentation is the task of identifying the spatial location of a tumor. It is a pixel-level prediction where each pixel is classified as a tumor or background. The most popular benchmark for this task is the BraTS dataset. The models are typically evaluated with the Dice Score metric.

Papers

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Showing 501–510 of 786 papers

TitleStatusHype
A Performance-Consistent and Computation-Efficient CNN System for High-Quality Automated Brain Tumor Segmentation—0
Class Balanced PixelNet for Neurological Image Segmentation—0
Negligible effect of brain MRI data preprocessing for tumor segmentationCode0
Category Guided Attention Network for Brain Tumor Segmentation in MRICode0
Slice Imputation: Intermediate Slice Interpolation for Anisotropic 3D Medical Image Segmentation—0
ME-Net: Multi-Encoder Net Framework for Brain Tumor Segmentation—0
Artificial Intelligence Solution for Effective Treatment Planning for Glioblastoma Patients—0
Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusion—0
Joint brain tumor segmentation from multi MR sequences through a deep convolutional neural network—0
Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network—0
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