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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 661–670 of 786 papers

TitleStatusHype
Brain tumour segmentation using a triplanar ensemble of U-NetsCode0
Head and Neck Tumor Segmentation of MRI from Pre- and Mid-radiotherapy with Pre-training, Data Augmentation and Dual Flow UNetCode0
Brain Tumor Segmentation using Enhanced U-Net Model with Empirical AnalysisCode0
Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice LossCode0
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT VolumesCode0
H2ASeg: Hierarchical Adaptive Interaction and Weighting Network for Tumor Segmentation in PET/CT ImagesCode0
SmoothSegNet: A Global-Local Framework for Liver Tumor Segmentation with Clinical KnowledgeInformed Label SmoothingCode0
QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality PredictionCode0
AGD-Autoencoder: Attention Gated Deep Convolutional Autoencoder for Brain Tumor SegmentationCode0
To deform or not: treatment-aware longitudinal registration for breast DCE-MRI during neoadjuvant chemotherapy via unsupervised keypoints detectionCode0
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