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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 511–520 of 786 papers

TitleStatusHype
Automated ensemble method for pediatric brain tumor segmentation—0
Multimodal 3D Brain Tumor Segmentation with Adversarial Training and Conditional Random Field—0
Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusion—0
Multi-Modal Brain Tumor Segmentation via 3D Multi-Scale Self-attention and Cross-attention—0
Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS 2022 Challenge Solution—0
Multi Modal Convolutional Neural Networks for Brain Tumor Segmentation—0
Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Review—0
Automated Ensemble-Based Segmentation of Adult Brain Tumors: A Novel Approach Using the BraTS AFRICA Challenge Data—0
Multimodal Learning With Intraoperative CBCT & Variably Aligned Preoperative CT Data To Improve Segmentation—0
Multimodal MRI brain tumor segmentation using random forests with features learned from fully convolutional neural network—0
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