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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 351–360 of 786 papers

TitleStatusHype
Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation—0
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation—0
FedPID: An Aggregation Method for Federated Learning—0
FedPIDAvg: A PID controller inspired aggregation method for Federated Learning—0
Few-Shot Generation of Brain Tumors for Secure and Fair Data Sharing—0
3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution Architectures—0
Flexible Fusion Network for Multi-modal Brain Tumor Segmentation—0
Brain Tumor Segmentation Network Using Attention-based Fusion and Spatial Relationship Constraint—0
Focus, Segment and Erase: An Efficient Network for Multi-Label Brain Tumor Segmentation—0
Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation—0
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