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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

Showing 231240 of 786 papers

TitleStatusHype
GuideGen: A Text-Guided Framework for Full-torso Anatomy and CT Volume GenerationCode0
3D-DDA: 3D Dual-Domain Attention for Brain Tumor SegmentationCode0
3D Medical Image Segmentation based on multi-scale MPU-NetCode0
H2ASeg: Hierarchical Adaptive Interaction and Weighting Network for Tumor Segmentation in PET/CT ImagesCode0
Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-oncology (I3CR-WANO)Code0
Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain AdaptationCode0
Generative Style Transfer for MRI Image Segmentation: A Case of Glioma Segmentation in Sub-Saharan AfricaCode0
Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice LossCode0
Glioblastoma Multiforme Prognosis: MRI Missing Modality Generation, Segmentation and Radiogenomic Survival PredictionCode0
Brain Tumor Segmentation Based on Deep Learning, Attention Mechanisms, and Energy-Based Uncertainty PredictionCode0
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