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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 126–150 of 786 papers

TitleStatusHype
Inter-slice Context Residual Learning for 3D Medical Image SegmentationCode1
DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasetsCode1
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor SegmentationCode1
DR-Unet104 for Multimodal MRI brain tumor segmentationCode1
Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for brain tumor segmentation: BraTS 2020 challengeCode1
nnU-Net for Brain Tumor SegmentationCode1
Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solutionCode1
What is the best data augmentation for 3D brain tumor segmentation?Code1
ivadomed: A Medical Imaging Deep Learning ToolboxCode1
Abstracting Deep Neural Networks into Concept Graphs for Concept Level InterpretabilityCode1
Adding Seemingly Uninformative Labels Helps in Low Data RegimesCode1
PSIGAN: Joint probabilistic segmentation and image distribution matching for unpaired cross-modality adaptation based MRI segmentationCode1
Deep Learning Based Brain Tumor Segmentation: A SurveyCode1
CANet: Context Aware Network for 3D Brain Glioma SegmentationCode1
Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNetCode1
3D Self-Supervised Methods for Medical ImagingCode1
Neural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance ImagingCode1
Multi-Modality Generative Adversarial Networks with Tumor Consistency Loss for Brain MR Image SynthesisCode1
DeepSeg: Deep Neural Network Framework for Automatic Brain Tumor Segmentation using Magnetic Resonance FLAIR ImagesCode1
Multi-Scale Supervised 3D U-Net for Kidneys and Kidney Tumor SegmentationCode1
Weakly supervised multiple instance learning histopathological tumor segmentationCode1
Attention-Guided Version of 2D UNet for Automatic Brain Tumor SegmentationCode1
Deep Learning-Based Concurrent Brain Registration and Tumor SegmentationCode1
Vox2Vox: 3D-GAN for Brain Tumour SegmentationCode1
Synthesize then Compare: Detecting Failures and Anomalies for Semantic SegmentationCode1
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