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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 241–250 of 786 papers

TitleStatusHype
Cross-Organ Domain Adaptive Neural Network for Pancreatic Endoscopic Ultrasound Image Segmentation—0
BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023—0
ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network—0
CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 dataset—0
CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation—0
BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectives—0
A Feasibility study for Deep learning based automated brain tumor segmentation using Magnetic Resonance Images—0
DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans—0
DDU-Nets: Distributed Dense Model for 3D MRI Brain Tumor Segmentation—0
Domain Game: Disentangle Anatomical Feature for Single Domain Generalized Segmentation—0
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