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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 101–110 of 786 papers

TitleStatusHype
AEPL: Automated and Editable Prompt Learning for Brain Tumor Segmentation—0
Multi-Layer Feature Fusion with Cross-Channel Attention-Based U-Net for Kidney Tumor Segmentation—0
Segmentation of Pediatric Brain Tumors using a Radiologically informed, Deep Learning Cascade—0
Gradient Map-Assisted Head and Neck Tumor Segmentation: A Pre-RT to Mid-RT Approach in MRI-Guided RadiotherapyCode0
Two-Stage Approach for Brain MR Image Synthesis: 2D Image Synthesis and 3D Refinement—0
SmoothSegNet: A Global-Local Framework for Liver Tumor Segmentation with Clinical KnowledgeInformed Label SmoothingCode0
ONCOPILOT: A Promptable CT Foundation Model For Solid Tumor Evaluation—0
Federated brain tumor segmentation: an extensive benchmark—0
Optimizing Medical Image Segmentation with Advanced Decoder DesignCode0
Mind the Gap: Promoting Missing Modality Brain Tumor Segmentation with Alignment—0
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