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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
3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context—0
Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival PredictionCode0
A Modality-Adaptive Method for Segmenting Brain Tumors and Organs-at-Risk in Radiation Therapy Planning—0
3D RoI-aware U-Net for Accurate and Efficient Colorectal Tumor SegmentationCode0
A Hybrid Framework for Tumor Saliency Estimation—0
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
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