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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 581–590 of 786 papers

TitleStatusHype
KMD: Koopman Multi-modality Decomposition for Generalized Brain Tumor Segmentation under Incomplete Modalities—0
Knowledge distillation from multi-modal to mono-modal segmentation networks—0
Large-Kernel Attention for 3D Medical Image Segmentation—0
Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities—0
LATUP-Net: A Lightweight 3D Attention U-Net with Parallel Convolutions for Brain Tumor Segmentation—0
Learning Data Augmentation for Brain Tumor Segmentation with Coarse-to-Fine Generative Adversarial Networks—0
Learning Multi-Modal Brain Tumor Segmentation from Privileged Semi-Paired MRI Images with Curriculum Disentanglement Learning—0
Learning to Learn Unlearned Feature for Brain Tumor Segmentation—0
Leveraging Clinical Characteristics for Improved Deep Learning-Based Kidney Tumor Segmentation on CT—0
Leveraging Human Selective Attention for Medical Image Analysis with Limited Training Data—0
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