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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 441–450 of 786 papers

TitleStatusHype
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation—0
A Generalized Surface Loss for Reducing the Hausdorff Distance in Medical Imaging SegmentationCode0
SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Images—0
Exploiting Partial Common Information Microstructure for Multi-Modal Brain Tumor SegmentationCode0
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization—0
Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma SegmentationCode0
Neural Gas Network Image Features and Segmentation for Brain Tumor Detection Using Magnetic Resonance Imaging DataCode0
Spatially Covariant Lesion Segmentation—0
Multi-Scanner Canine Cutaneous Squamous Cell Carcinoma Histopathology DatasetCode0
Artificial Intelligence Model for Tumoral Clinical Decision Support Systems—0
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