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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 741–750 of 786 papers

TitleStatusHype
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithmCode0
Adaptive Active Contour Model for Brain Tumor SegmentationCode0
TexLiverNet: Leveraging Medical Knowledge and Spatial-Frequency Perception for Enhanced Liver Tumor SegmentationCode0
AFFSegNet: Adaptive Feature Fusion Segmentation Network for Microtumors and Multi-Organ SegmentationCode0
multiPI-TransBTS: A Multi-Path Learning Framework for Brain Tumor Image Segmentation Based on Multi-Physical InformationCode0
3D-DDA: 3D Dual-Domain Attention for Brain Tumor SegmentationCode0
Enhancing Incomplete Multi-modal Brain Tumor Segmentation with Intra-modal Asymmetry and Inter-modal DependencyCode0
Selective Complementary Feature Fusion and Modal Feature Compression Interaction for Brain Tumor SegmentationCode0
Assessing Test-time Variability for Interactive 3D Medical Image Segmentation with Diverse Point PromptsCode0
Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learningCode0
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