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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 541–550 of 786 papers

TitleStatusHype
Tumor Location-weighted MRI-Report Contrastive Learning: A Framework for Improving the Explainability of Pediatric Brain Tumor Diagnosis—0
Tumor segmentation on whole slide images: training or prompting?—0
TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks—0
Two-Stage Approach for Brain MR Image Synthesis: 2D Image Synthesis and 3D Refinement—0
Two-stage MR Image Segmentation Method for Brain Tumors based on Attention Mechanism—0
Two Stage Segmentation of Cervical Tumors using PocketNet—0
Uncertainty-driven refinement of tumor-core segmentation using 3D-to-2D networks with label uncertainty—0
Uncertainty-Guided Coarse-to-Fine Tumor Segmentation with Anatomy-Aware Post-Processing—0
Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation—0
Unified HT-CNNs Architecture: Transfer Learning for Segmenting Diverse Brain Tumors in MRI from Gliomas to Pediatric Tumors—0
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