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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 671–680 of 786 papers

TitleStatusHype
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking ResultsCode0
GuideGen: A Text-Guided Framework for Full-torso Anatomy and CT Volume GenerationCode0
Low-Contrast-Enhanced Contrastive Learning for Semi-Supervised Endoscopic Image SegmentationCode0
A fuzzy rank-based ensemble of CNN models for MRI segmentationCode0
Gradient Map-Assisted Head and Neck Tumor Segmentation: A Pre-RT to Mid-RT Approach in MRI-Guided RadiotherapyCode0
3D Medical Image Segmentation based on multi-scale MPU-NetCode0
Autofocus Layer for Semantic SegmentationCode0
Glioma Segmentation with Cascaded UnetCode0
Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor SegmentationCode0
Brain Tumor Segmentation Based on Deep Learning, Attention Mechanisms, and Energy-Based Uncertainty PredictionCode0
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