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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 541–550 of 786 papers

TitleStatusHype
A Transformer-based Generative Adversarial Network for Brain Tumor Segmentation—0
A CADe System for Gliomas in Brain MRI using Convolutional Neural Networks—0
Neural Network-Based Automatic Liver Tumor Segmentation With Random Forest-Based Candidate Filtering—0
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging—0
Non Parametric Data Augmentations Improve Deep-Learning based Brain Tumor Segmentation—0
A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction—0
A Structural Graph-Based Method for MRI Analysis—0
ASLseg: Adapting SAM in the Loop for Semi-supervised Liver Tumor Segmentation—0
ONCOPILOT: A Promptable CT Foundation Model For Solid Tumor Evaluation—0
Quantifying uncertainty in lung cancer segmentation with foundation models applied to mixed-domain datasets—0
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