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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 321–330 of 786 papers

TitleStatusHype
3D Brainformer: 3D Fusion Transformer for Brain Tumor Segmentation—0
FedPIDAvg: A PID controller inspired aggregation method for Federated Learning—0
Topology-Aware Focal Loss for 3D Image Segmentation—0
When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation—0
Two-stage MR Image Segmentation Method for Brain Tumors based on Attention Mechanism—0
The Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planning—0
Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image SegmentationCode1
Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning—0
Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging—0
FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures For Medical Imaging SegmentationCode0
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