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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 731–740 of 786 papers

TitleStatusHype
SegAN: Adversarial Network with Multi-scale L_1 Loss for Medical Image SegmentationCode0
Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor SegmentationCode0
UKAN-EP: Enhancing U-KAN with Efficient Attention and Pyramid Aggregation for 3D Multi-Modal MRI Brain Tumor SegmentationCode0
Segment anything model for head and neck tumor segmentation with CT, PET and MRI multi-modality imagesCode0
Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node MetastasisCode0
Utilizing Attention, Linked Blocks, And Pyramid Pooling To Propel Brain Tumor Segmentation In 3DCode0
Task-oriented Uncertainty Collaborative Learning for Label-Efficient Brain Tumor SegmentationCode0
Multi-modal Evidential Fusion Network for Trustworthy PET/CT Tumor SegmentationCode0
Meta-Learned Modality-Weighted Knowledge Distillation for Robust Multi-Modal Learning with Missing DataCode0
A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor SegmentationCode0
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