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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 626–650 of 786 papers

TitleStatusHype
Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation—0
United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast MRI—0
Seeing Beyond Cancer: Multi-Institutional Validation of Object Localization and 3D Semantic Segmentation using Deep Learning for Breast MRI—0
SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentation—0
Segment Anything Model for Brain Tumor Segmentation—0
Unpaired cross-modality educed distillation (CMEDL) for medical image 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
Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Network—0
Segmentation of Kidney Tumors on Non-Contrast CT Images using Protuberance Detection Network—0
Segmentation of Liver Lesions with Reduced Complexity Deep Models—0
Segmentation of Lung Tumor from CT Images using Deep Supervision—0
Segmentation of Pediatric Brain Tumors using a Radiologically informed, Deep Learning Cascade—0
Segmenting Brain Tumors with Symmetry—0
Unsupervised Brain Tumor Segmentation with Image-based Prompts—0
Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging—0
3D Kidneys and Kidney Tumor Semantic Segmentation using Boundary-Aware Networks—0
Self-calibrated convolution towards glioma segmentation—0
An Efficient Solution for Breast Tumor Segmentation and Classification in Ultrasound Images Using Deep Adversarial Learning—0
Self-semantic contour adaptation for cross modality brain tumor segmentation—0
Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik's Cube—0
Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout—0
Self-Supervised Learning for Organs At Risk and Tumor Segmentation with Uncertainty Quantification—0
Self-supervised learning improves robustness of deep learning lung tumor segmentation to CT imaging differences—0
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