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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 431–440 of 786 papers

TitleStatusHype
Kidney and Kidney Tumor Segmentation using a Logical Ensemble of U-nets with Volumetric Validation—0
Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge—0
KMD: Koopman Multi-modality Decomposition for Generalized Brain Tumor Segmentation under Incomplete Modalities—0
BC-MRI-SEG: A Breast Cancer MRI Tumor Segmentation Benchmark—0
Knowledge distillation from multi-modal to mono-modal segmentation networks—0
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems—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
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