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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 301–325 of 786 papers

TitleStatusHype
Anisotropic Hybrid Networks for liver tumor segmentation with uncertainty quantification—0
3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context—0
Brain Tumor Classification by Cascaded Multiscale Multitask Learning Framework Based on Feature Aggregation—0
Brain MRI study for glioma segmentation using convolutional neural networks and original post-processing techniques with low computational demand—0
Targeted Neural Architectures in Multi-Objective Frameworks for Complete Glioma Characterization from Multimodal MRI—0
Ensemble Learning and 3D Pix2Pix for Comprehensive Brain Tumor Analysis in Multimodal MRI—0
Enhancing SAM with Efficient Prompting and Preference Optimization for Semi-supervised Medical Image Segmentation—0
End-to-End Boundary Aware Networks for Medical Image Segmentation—0
An Inductive Transfer Learning Approach using Cycle-consistent Adversarial Domain Adaptation with Application to Brain Tumor Segmentation—0
Focus, Segment and Erase: An Efficient Network for Multi-Label Brain Tumor Segmentation—0
Fully Automatic Brain Tumor Segmentation using a Normalized Gaussian Bayesian Classifier and 3D Fluid Vector Flow—0
Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networks—0
Enhancing Privacy: The Utility of Stand-Alone Synthetic CT and MRI for Tumor and Bone Segmentation—0
Bottleneck Supervised U-Net for Pixel-wise Liver and Tumor Segmentation—0
An Exceptional Dataset For Rare Pancreatic Tumor Segmentation—0
Enhancing MRI Brain Tumor Segmentation with an Additional Classification Network—0
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation—0
Towards annotation-efficient segmentation via image-to-image translation—0
FedPID: An Aggregation Method for Federated Learning—0
A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation—0
Mask Mining for Improved Liver Lesion Segmentation—0
Ensemble Learning with Residual Transformer for Brain Tumor Segmentation—0
Enhancing Brain Tumor Classification Using TrAdaBoost and Multi-Classifier Deep Learning Approaches—0
End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation—0
FedPIDAvg: A PID controller inspired aggregation method for Federated Learning—0
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