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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 561–570 of 786 papers

TitleStatusHype
Weakly supervised pan-cancer segmentation tool—0
Deep segmentation networks predict survival of non-small cell lung cancer—0
When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation—0
Whole-body tumor segmentation of 18F -FDG PET/CT using a cascaded and ensembled convolutional neural networks—0
Within-Brain Classification for 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
Interactive Image Selection and Training for Brain Tumor Segmentation Network—0
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