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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 451–475 of 786 papers

TitleStatusHype
QuantU-Net: Efficient Wearable Medical Imaging Using Bitwidth as a Trainable Parameter—0
QuickTumorNet: Fast Automatic Multi-Class Segmentation of Brain Tumors—0
Qutrit-inspired Fully Self-supervised Shallow Quantum Learning Network for Brain Tumor Segmentation—0
Radiomics as a measure superior to the Dice similarity coefficient for tumor segmentation performance evaluation—0
RCA-IUnet: A residual cross-spatial attention guided inception U-Net model for tumor segmentation in breast ultrasound imaging—0
Recommender Engine Driven Client Selection in Federated Brain Tumor Segmentation—0
Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs—0
Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation—0
Region of Interest Identification for Brain Tumors in Magnetic Resonance Images—0
Relevance analysis of MRI sequences for automatic liver tumor segmentation—0
Rel-UNet: Reliable Tumor Segmentation via Uncertainty Quantification in nnU-Net—0
Response monitoring of breast cancer on DCE-MRI using convolutional neural network-generated seed points and constrained volume growing—0
RobU-Net: a heuristic robust multi-class brain tumor segmentation approaches for MRI scans—0
Robust Automatic Whole Brain Extraction on Magnetic Resonance Imaging of Brain Tumor Patients using Dense-Vnet—0
Robustifying deep networks for image segmentation—0
Robust Learning Protocol for Federated Tumor Segmentation Challenge—0
Robustness of Brain Tumor Segmentation—0
Robust Pancreatic Ductal Adenocarcinoma Segmentation with Multi-Institutional Multi-Phase Partially-Annotated CT Scans—0
Robust Tumor Segmentation with Hyperspectral Imaging and Graph Neural Networks—0
Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation—0
Seeing Beyond Cancer: Multi-Institutional Validation of Object Localization and 3D Semantic Segmentation using Deep Learning for Breast MRI—0
Segment Anything Model for Brain Tumor Segmentation—0
Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging—0
Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks—0
Segmentation of brain tumor on magnetic resonance imaging using a convolutional architecture—0
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