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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 521–530 of 786 papers

TitleStatusHype
Multimodal Self-Supervised Learning for Medical Image Analysis—0
Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentation—0
Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting—0
Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation—0
Multi-Resolution 3D CNN for MRI Brain Tumor Segmentation and Survival Prediction—0
Automated 3D Tumor Segmentation using Temporal Cubic PatchGAN (TCuP-GAN)—0
Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation—0
Multi Scale Supervised 3D U-Net for Kidney and Tumor Segmentation—0
Automated 3D Segmentation of Kidneys and Tumors in MICCAI KiTS 2023 Challenge—0
A Cascaded Deep-Learning Framework for Segmentation of Metastatic Brain Tumors Before and After Stereotactic Radiation Therapy—0
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