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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 201–225 of 786 papers

TitleStatusHype
A fuzzy rank-based ensemble of CNN models for MRI segmentationCode0
Breast Tumor Segmentation and Shape Classification in Mammograms using Generative Adversarial and Convolutional Neural NetworkCode0
3D-TransUNet for Brain Metastases Segmentation in the BraTS2023 ChallengeCode0
UKAN-EP: Enhancing U-KAN with Efficient Attention and Pyramid Aggregation for 3D Multi-Modal MRI Brain Tumor SegmentationCode0
3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRICode0
Category Guided Attention Network for Brain Tumor Segmentation in MRICode0
Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor SegmentationCode0
A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor SegmentationCode0
SmoothSegNet: A Global-Local Framework for Liver Tumor Segmentation with Clinical KnowledgeInformed Label SmoothingCode0
3D U-Net Based Brain Tumor Segmentation and Survival Days PredictionCode0
Brain tumour segmentation using a triplanar ensemble of U-NetsCode0
Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-oncology (I3CR-WANO)Code0
Improved automated lesion segmentation in whole-body FDG/PET-CT via Test-Time AugmentationCode0
Improving the U-Net Configuration for Automated Delineation of Head and Neck Cancer on MRICode0
Intensity-Spatial Dual Masked Autoencoder for Multi-Scale Feature Learning in Chest CT SegmentationCode0
Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing ModalitiesCode0
Brain Tumor Segmentation using Enhanced U-Net Model with Empirical AnalysisCode0
MRI Tumor Segmentation with Densely Connected 3D CNNCode0
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS ChallengeCode0
Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-guided RadiotherapyCode0
Head and Neck Tumor Segmentation of MRI from Pre- and Mid-radiotherapy with Pre-training, Data Augmentation and Dual Flow UNetCode0
Arbitrary Scale Super-Resolution for Brain MRI ImagesCode0
H2ASeg: Hierarchical Adaptive Interaction and Weighting Network for Tumor Segmentation in PET/CT ImagesCode0
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT VolumesCode0
Hybrid-Fusion Transformer for Multisequence MRICode0
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