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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 711–720 of 786 papers

TitleStatusHype
Cascaded V-Net using ROI masks for brain tumor segmentation—0
Deep Learning with Mixed Supervision for Brain Tumor Segmentation—0
Brain Tumor Segmentation using an Ensemble of 3D U-Nets and Overall Survival Prediction using Radiomic Features—0
3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training—0
Multi-Task Generative Adversarial Network for Handling Imbalanced Clinical Data—0
Response monitoring of breast cancer on DCE-MRI using convolutional neural network-generated seed points and constrained volume growing—0
A Pretrained DenseNet Encoder for Brain Tumor Segmentation—0
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS ChallengeCode0
RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scansCode0
A Volumetric Convolutional Neural Network for Brain Tumor Segmentation—0
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