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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 401–410 of 786 papers

TitleStatusHype
Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation—0
PA-Seg: Learning from Point Annotations for 3D Medical Image Segmentation using Contextual Regularization and Cross Knowledge DistillationCode1
Analyzing Deep Learning Based Brain Tumor Segmentation with Missing MRI Modalities—0
Deep Learning and Health Informatics for Smart Monitoring and Diagnosis—0
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation—0
A Transformer-based Generative Adversarial Network for Brain Tumor Segmentation—0
PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training—0
High-Resolution Swin Transformer for Automatic Medical Image SegmentationCode1
Adaptive Active Contour Model for Brain Tumor SegmentationCode0
Large-Kernel Attention for 3D Medical Image Segmentation—0
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