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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 271–280 of 786 papers

TitleStatusHype
Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional NetworksCode0
Glioblastoma Tumor Segmentation using an Ensemble of Vision TransformersCode0
FR-MRInet: A Deep Convolutional Encoder-Decoder for Brain Tumor Segmentation with Relu-RGB and Sliding-windowCode0
A Generalized Surface Loss for Reducing the Hausdorff Distance in Medical Imaging SegmentationCode0
Adaptive feature recombination and recalibration for semantic segmentation: application to brain tumor segmentation in MRICode0
Glioma Segmentation with Cascaded UnetCode0
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS ChallengeCode0
AutoPET Challenge: Combining nn-Unet with Swin UNETR Augmented by Maximum Intensity Projection ClassifierCode0
AutoPET Challenge 2022: Automatic Segmentation of Whole-body Tumor Lesion Based on Deep Learning and FDG PET/CTCode0
Adaptive Active Contour Model for Brain Tumor SegmentationCode0
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