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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 531–540 of 786 papers

TitleStatusHype
Exploring Feature Representation Learning for Semi-supervised Medical Image SegmentationCode0
Segmentation of Lung Tumor from CT Images using Deep Supervision—0
FedCostWAvg: A new averaging for better Federated Learning—0
Feature-enhanced Generation and Multi-modality Fusion based Deep Neural Network for Brain Tumor Segmentation with Missing MR Modalities—0
A Tri-attention Fusion Guided Multi-modal Segmentation Network—0
Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs—0
Correlation between image quality metrics of magnetic resonance images and the neural network segmentation accuracy—0
Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image SegmentationCode0
Combining CNNs With Transformer for Multimodal 3D MRI Brain Tumor Segmentation With Self-Supervised Pretraining—0
Optimized U-Net for Brain Tumor SegmentationCode0
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