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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 331–340 of 786 papers

TitleStatusHype
Experimenting with Knowledge Distillation techniques for performing Brain Tumor Segmentation—0
AdaViT: Adaptive Vision Transformer for Flexible Pretrain and Finetune with Variable 3D Medical Image Modalities—0
A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation—0
Exploring 3D U-Net Training Configurations and Post-Processing Strategies for the MICCAI 2023 Kidney and Tumor Segmentation Challenge—0
Mask Mining for Improved Liver Lesion Segmentation—0
Exploring Adult Glioma through MRI: A Review of Publicly Available Datasets to Guide Efficient Image Analysis—0
Fully Automated Tumor Segmentation for Brain MRI data using Multiplanner UNet—0
Exploring Structure-Wise Uncertainty for 3D Medical Image Segmentation—0
Enhancing Brain Tumor Classification Using TrAdaBoost and Multi-Classifier Deep Learning Approaches—0
End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation—0
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