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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 681–690 of 786 papers

TitleStatusHype
SurgicalVLM-Agent: Towards an Interactive AI Co-Pilot for Pituitary Surgery—0
3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context—0
VIViT: Variable-Input Vision Transformer Framework for 3D MR Image Segmentation—0
SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Images—0
Swin UNETR++: Advancing Transformer-Based Dense Dose Prediction Towards Fully Automated Radiation Oncology Treatments—0
Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology—0
Combining CNN and Hybrid Active Contours for Head and Neck Tumor Segmentation in CT and PET images—0
Combining CNNs With Transformer for Multimodal 3D MRI Brain Tumor Segmentation With Self-Supervised Pretraining—0
Comparative Analysis of Image Enhancement Techniques for Brain Tumor Segmentation: Contrast, Histogram, and Hybrid Approaches—0
SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation—0
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