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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 531–540 of 786 papers

TitleStatusHype
Multi-Slice Dense-Sparse Learning for Efficient Liver and Tumor Segmentation—0
Multi-stage Deep Layer Aggregation for Brain Tumor Segmentation—0
TransMed: Transformers Advance Multi-modal Medical Image Classification—0
Multi-Task Generative Adversarial Network for Handling Imbalanced Clinical Data—0
Multi-task Learning To Improve Semantic Segmentation Of CBCT Scans Using Image Reconstruction—0
Multi-Threshold Attention U-Net (MTAU) based Model for Multimodal Brain Tumor Segmentation in MRI scans—0
A Unified Conditional Disentanglement Framework for Multimodal Brain MR Image Translation—0
Attention Xception UNet (AXUNet): A Novel Combination of CNN and Self-Attention for Brain Tumor Segmentation—0
Treatment-aware Diffusion Probabilistic Model for Longitudinal MRI Generation and Diffuse Glioma Growth Prediction—0
A Tri-attention Fusion Guided Multi-modal Segmentation Network—0
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