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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 431–440 of 786 papers

TitleStatusHype
Category Guided Attention Network for Brain Tumor Segmentation in MRICode0
Translation Consistent Semi-supervised Segmentation for 3D Medical ImagesCode1
ME-Net: Multi-Encoder Net Framework for Brain Tumor Segmentation—0
Slice Imputation: Intermediate Slice Interpolation for Anisotropic 3D Medical Image Segmentation—0
Self Pre-training with Masked Autoencoders for Medical Image Classification and SegmentationCode1
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
Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network—0
Joint brain tumor segmentation from multi MR sequences through a deep convolutional neural network—0
Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image SegmentationCode1
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