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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 101–150 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
Modality-aware Mutual Learning for Multi-modal Medical Image SegmentationCode1
TumorCP: A Simple but Effective Object-Level Data Augmentation for Tumor SegmentationCode1
The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic ClassificationCode1
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing ModalitiesCode1
Medical Image Segmentation Using Squeeze-and-Expansion TransformersCode1
The Federated Tumor Segmentation (FeTS) ChallengeCode1
mlf-core: a framework for deterministic machine learningCode1
Brain Tumor Segmentation and Survival Prediction using 3D Attention UNetCode1
TransBTS: Multimodal Brain Tumor Segmentation Using TransformerCode1
ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly SegmentationCode1
Representation Disentanglement for Multi-modal brain MR AnalysisCode1
Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT ImagesCode1
NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentationCode1
RFNet: Region-Aware Fusion Network for Incomplete Multi-Modal Brain Tumor SegmentationCode1
MRI brain tumor segmentation and uncertainty estimation using 3D-UNet architecturesCode1
Annotation-efficient deep learning for automatic medical image segmentationCode1
Esophageal Tumor Segmentation in CT Images using Dilated Dense Attention Unet (DDAUnet)Code1
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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