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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 421–430 of 786 papers

TitleStatusHype
mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor SegmentationCode1
Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images—0
FedMix: Mixed Supervised Federated Learning for Medical Image SegmentationCode1
A Performance-Consistent and Computation-Efficient CNN System for High-Quality Automated Brain Tumor Segmentation—0
Preoperative brain tumor imaging: models and software for segmentation and standardized reportingCode1
Class Balanced PixelNet for Neurological Image Segmentation—0
Multi-View Hypercomplex Learning for Breast Cancer ScreeningCode1
Negligible effect of brain MRI data preprocessing for tumor segmentationCode0
SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalitiesCode1
UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image SegmentationCode3
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