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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 271–280 of 786 papers

TitleStatusHype
Advanced Tumor Segmentation in Medical Imaging: An Ensemble Approach for BraTS 2023 Adult Glioma and Pediatric Tumor Tasks—0
Brain Tumor Segmentation using 3D-CNNs with Uncertainty Estimation—0
Brain Tumor Segmentation on MRI with Missing Modalities—0
A Prior Knowledge Based Tumor and Tumoral Subregion Segmentation Tool for Pediatric Brain Tumors—0
3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training—0
Brain Tumor Segmentation Network Using Attention-based Fusion and Spatial Relationship Constraint—0
Brain Tumor Segmentation in MRI Images with 3D U-Net and Contextual Transformer—0
A Pretrained DenseNet Encoder for Brain Tumor Segmentation—0
Brain Tumor Segmentation from MRI Images using Deep Learning Techniques—0
Brain Tumor Segmentation by Cascaded Deep Neural Networks Using Multiple Image Scales—0
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