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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 711–720 of 786 papers

TitleStatusHype
Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation using Rein to Fine-tune Vision Foundation Models—0
Cross-Organ Domain Adaptive Neural Network for Pancreatic Endoscopic Ultrasound Image Segmentation—0
CBCTLiTS: A Synthetic, Paired CBCT/CT Dataset For Segmentation And Style Transfer—0
CASPIANET++: A Multidimensional Channel-Spatial Asymmetric Attention Network with Noisy Student Curriculum Learning Paradigm for Brain Tumor Segmentation—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
Cascaded Volumetric Convolutional Network for Kidney Tumor Segmentation from CT volumes—0
DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images—0
Cascaded V-Net using ROI masks for brain tumor segmentation—0
DDU-Nets: Distributed Dense Model for 3D MRI Brain Tumor Segmentation—0
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