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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 411–420 of 786 papers

TitleStatusHype
Improving the Segmentation of Pediatric Low-Grade Gliomas through Multitask Learning—0
A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation—0
Incomplete Multi-modal Brain Tumor Segmentation via Learnable Sorting State Space Model—0
Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI—0
Integrating cross-modality hallucinated MRI with CT to aid mediastinal lung tumor segmentation—0
Integrating Edges into U-Net Models with Explainable Activation Maps for Brain Tumor Segmentation using MR Images—0
3D Brainformer: 3D Fusion Transformer for Brain Tumor Segmentation—0
Adaptive Smooth Activation for Improved Disease Diagnosis and Organ Segmentation from Radiology Scans—0
Interactive Image Selection and Training for Brain Tumor Segmentation Network—0
Benefits of Linear Conditioning with Metadata for Image Segmentation—0
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