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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 601–625 of 786 papers

TitleStatusHype
E^2Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans—0
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-Domain Image Completion for Random Missing Input Data—0
Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNetCode1
Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional NetworksCode0
DSU-net: Dense SegU-net for automatic head-and-neck tumor segmentation in MR images—0
3D Self-Supervised Methods for Medical ImagingCode1
Robust Automatic Whole Brain Extraction on Magnetic Resonance Imaging of Brain Tumor Patients using Dense-Vnet—0
A Review on End-To-End Methods for Brain Tumor Segmentation and Overall Survival Prediction—0
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation ProblemsCode0
Neural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance ImagingCode1
An Inductive Transfer Learning Approach using Cycle-consistent Adversarial Domain Adaptation with Application to Brain Tumor Segmentation—0
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Images for Segmentation—0
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
Joint Liver Lesion Segmentation and Classification via Transfer Learning—0
Multi-Scale Supervised 3D U-Net for Kidneys and Kidney Tumor SegmentationCode1
Decentralized Differentially Private Segmentation with PATE—0
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
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