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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 621–630 of 786 papers

TitleStatusHype
MRI Brain Tumor Segmentation using Random Forests and Fully Convolutional Networks—0
Multi-class Brain Tumor Segmentation using Graph Attention Network—0
Multiclass MRI Brain Tumor Segmentation using 3D Attention-based U-Net—0
Multiclass Spinal Cord Tumor Segmentation on MRI with Deep Learning—0
Multi-Decoder Networks with Multi-Denoising Inputs for Tumor Segmentation—0
Multi-Domain Image Completion for Random Missing Input Data—0
Multi-encoder nnU-Net outperforms Transformer models with self-supervised pretraining—0
Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation—0
Multi-Layer Feature Fusion with Cross-Channel Attention-Based U-Net for Kidney Tumor Segmentation—0
Multimodal 3D Brain Tumor Segmentation with Adversarial Training and Conditional Random Field—0
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