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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 781–786 of 786 papers

TitleStatusHype
End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks—0
End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation—0
Enhancing Brain Tumor Classification Using TrAdaBoost and Multi-Classifier Deep Learning Approaches—0
AMM-Diff: Adaptive Multi-Modality Diffusion Network for Missing Modality Imputation—0
All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation—0
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation—0
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