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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 171–180 of 786 papers

TitleStatusHype
AMM-Diff: Adaptive Multi-Modality Diffusion Network for Missing Modality Imputation—0
Automated head and neck tumor segmentation from 3D PET/CT—0
Automated ensemble method for pediatric brain tumor segmentation—0
A Cascaded Deep-Learning Framework for Segmentation of Metastatic Brain Tumors Before and After Stereotactic Radiation Therapy—0
Automated Ensemble-Based Segmentation of Adult Brain Tumors: A Novel Approach Using the BraTS AFRICA Challenge Data—0
Automated 3D Tumor Segmentation using Temporal Cubic PatchGAN (TCuP-GAN)—0
A CADe System for Gliomas in Brain MRI using Convolutional Neural Networks—0
Automated 3D Segmentation of Kidneys and Tumors in MICCAI KiTS 2023 Challenge—0
All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation—0
DM-SegNet: Dual-Mamba Architecture for 3D Medical Image Segmentation with Global Context Modeling—0
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