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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 101–110 of 786 papers

TitleStatusHype
Extending nn-UNet for brain tumor segmentationCode1
Diffusion Models for Implicit Image Segmentation EnsemblesCode1
FIBA: Frequency-Injection based Backdoor Attack in Medical Image AnalysisCode1
A Robust Volumetric Transformer for Accurate 3D Tumor SegmentationCode1
E1D3 U-Net for Brain Tumor Segmentation: Submission to the RSNA-ASNR-MICCAI BraTS 2021 ChallengeCode1
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor SegmentationCode1
DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CTCode1
A Joint Graph and Image Convolution Network for Automatic Brain Tumor SegmentationCode1
TumorCP: A Simple but Effective Object-Level Data Augmentation for Tumor SegmentationCode1
Modality-aware Mutual Learning for Multi-modal Medical Image SegmentationCode1
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