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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 621–630 of 786 papers

TitleStatusHype
Weakly supervised multiple instance learning histopathological tumor segmentationCode1
Arbitrary Scale Super-Resolution for Brain MRI ImagesCode0
Attention-Guided Version of 2D UNet for Automatic Brain Tumor SegmentationCode1
Volumetric Attention for 3D Medical Image Segmentation and Detection—0
Deep Learning-Based Concurrent Brain Registration and Tumor SegmentationCode1
Vox2Vox: 3D-GAN for Brain Tumour SegmentationCode1
Brain tumor segmentation with missing modalities via latent multi-source correlation representation—0
Synthesize then Compare: Detecting Failures and Anomalies for Semantic SegmentationCode1
Segmentation of brain tumor on magnetic resonance imaging using a convolutional architecture—0
DDU-Nets: Distributed Dense Model for 3D MRI Brain Tumor Segmentation—0
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