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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 741–750 of 786 papers

TitleStatusHype
A Hybrid Framework for Tumor Saliency Estimation—0
3D RoI-aware U-Net for Accurate and Efficient Colorectal Tumor SegmentationCode0
A CADe System for Gliomas in Brain MRI using Convolutional Neural Networks—0
Adaptive feature recombination and recalibration for semantic segmentation: application to brain tumor segmentation in MRICode0
Learning Data Augmentation for Brain Tumor Segmentation with Coarse-to-Fine Generative Adversarial Networks—0
Segmentation of Liver Lesions with Reduced Complexity Deep Models—0
Autofocus Layer for Semantic SegmentationCode0
Fast and Accurate Tumor Segmentation of Histology Images using Persistent Homology and Deep Convolutional Features—0
Crossbar-Net: A Novel Convolutional Network for Kidney Tumor Segmentation in CT Images—0
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 ChallengeCode1
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