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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 631–640 of 786 papers

TitleStatusHype
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
Improving the U-Net Configuration for Automated Delineation of Head and Neck Cancer on MRICode0
Co-Learning Feature Fusion Maps from PET-CT Images of Lung CancerCode0
Patient-Specific Real-Time Segmentation in Trackerless Brain UltrasoundCode0
Sine Wave Normalization for Deep Learning-Based Tumor Segmentation in CT/PET ImagingCode0
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural NetworksCode0
Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-oncology (I3CR-WANO)Code0
Intensity-Spatial Dual Masked Autoencoder for Multi-Scale Feature Learning in Chest CT SegmentationCode0
Category Guided Attention Network for Brain Tumor Segmentation in MRICode0
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