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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 351–375 of 786 papers

TitleStatusHype
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation—0
3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution Architectures—0
FedPID: An Aggregation Method for Federated Learning—0
FedPIDAvg: A PID controller inspired aggregation method for Federated Learning—0
Few-Shot Generation of Brain Tumors for Secure and Fair Data Sharing—0
Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation—0
Flexible Fusion Network for Multi-modal Brain Tumor Segmentation—0
Brain Tumor Segmentation Network Using Attention-based Fusion and Spatial Relationship Constraint—0
Focus, Segment and Erase: An Efficient Network for Multi-Label Brain Tumor Segmentation—0
Free-form Lesion Synthesis Using a Partial Convolution Generative Adversarial Network for Enhanced Deep Learning Liver Tumor Segmentation—0
Efficient Parameter Adaptation for Multi-Modal Medical Image Segmentation and Prognosis—0
Brain Tumor Segmentation using 3D-CNNs with Uncertainty Estimation—0
Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors—0
Fully Automated Tumor Segmentation for Brain MRI data using Multiplanner UNet—0
Fully Automatic Brain Tumor Segmentation using a Normalized Gaussian Bayesian Classifier and 3D Fluid Vector Flow—0
GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models—0
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Images for Segmentation—0
Efficient embedding network for 3D brain tumor segmentation—0
Efficient Brain Tumor Segmentation Using a Dual-Decoder 3D U-Net with Attention Gates (DDUNet)—0
Benefits of Linear Conditioning with Metadata for Image Segmentation—0
A Bayesian approach to tissue-fraction estimation for oncological PET segmentation—0
Image-level supervision and self-training for transformer-based cross-modality tumor segmentation—0
Incomplete Multi-modal Brain Tumor Segmentation via Learnable Sorting State Space Model—0
Glioblastoma Multiforme Patient Survival Prediction—0
Belief function-based semi-supervised learning for brain tumor segmentation—0
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