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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 91–100 of 786 papers

TitleStatusHype
Multimodal 3D Brain Tumor Segmentation with Adversarial Training and Conditional Random Field—0
Automatic brain tumor segmentation in 2D intra-operative ultrasound images using MRI tumor annotationsCode0
Intensity-Spatial Dual Masked Autoencoder for Multi-Scale Feature Learning in Chest CT SegmentationCode0
TexLiverNet: Leveraging Medical Knowledge and Spatial-Frequency Perception for Enhanced Liver Tumor SegmentationCode0
FedPID: An Aggregation Method for Federated Learning—0
MBDRes-U-Net: Multi-Scale Lightweight Brain Tumor Segmentation NetworkCode0
A New Logic For Pediatric Brain Tumor SegmentationCode0
Tumor Location-weighted MRI-Report Contrastive Learning: A Framework for Improving the Explainability of Pediatric Brain Tumor Diagnosis—0
Lung tumor segmentation in MRI mice scans using 3D nnU-Net with minimum annotations—0
Enhancing Brain Tumor Classification Using TrAdaBoost and Multi-Classifier Deep Learning Approaches—0
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