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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 771–780 of 786 papers

TitleStatusHype
On Enhancing Brain Tumor Segmentation Across Diverse Populations with Convolutional Neural NetworksCode0
One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor SegmentationCode0
Optimized U-Net for Brain Tumor SegmentationCode0
Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain AdaptationCode0
Optimizing Medical Image Segmentation with Advanced Decoder DesignCode0
A Weakly Supervised and Globally Explainable Learning Framework for Brain Tumor SegmentationCode0
Optimizing Synthetic Data for Enhanced Pancreatic Tumor SegmentationCode0
A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor SegmentationCode0
Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-guided RadiotherapyCode0
Semi-Supervised Variational Autoencoder for Survival PredictionCode0
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