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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 361–370 of 786 papers

TitleStatusHype
Comparative Analysis of Image Enhancement Techniques for Brain Tumor Segmentation: Contrast, Histogram, and Hybrid Approaches—0
Combining CNNs With Transformer for Multimodal 3D MRI Brain Tumor Segmentation With Self-Supervised Pretraining—0
A Tri-attention Fusion Guided Multi-modal Segmentation Network—0
Combining CNN and Hybrid Active Contours for Head and Neck Tumor Segmentation in CT and PET images—0
A Transformer-based Generative Adversarial Network for Brain Tumor Segmentation—0
AI Age Discrepancy: A Novel Parameter for Frailty Assessment in Kidney Tumor Patients—0
Clinical Inspired MRI Lesion Segmentation—0
Class Balanced PixelNet for Neurological Image Segmentation—0
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging—0
CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation—0
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