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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 571–580 of 786 papers

TitleStatusHype
Intraoperative Glioma Segmentation with YOLO + SAM for Improved Accuracy in Tumor Resection—0
Investigating certain choices of CNN configurations for brain lesion segmentation—0
Investigation of Network Architecture for Multimodal Head-and-Neck Tumor Segmentation—0
ISA-Net: Improved spatial attention network for PET-CT tumor segmentation—0
Is Long Range Sequential Modeling Necessary For Colorectal Tumor Segmentation?—0
Joint brain tumor segmentation from multi MR sequences through a deep convolutional neural network—0
Joint Liver and Hepatic Lesion Segmentation in MRI using a Hybrid CNN with Transformer Layers—0
Joint Liver Lesion Segmentation and Classification via Transfer Learning—0
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
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