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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 451–460 of 786 papers

TitleStatusHype
Investigation of Network Architecture for Multimodal Head-and-Neck Tumor Segmentation—0
Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS 2022 Challenge Solution—0
Robust Learning Protocol for Federated Tumor Segmentation Challenge—0
Towards fully automated deep-learning-based brain tumor segmentation: is brain extraction still necessary?Code0
M-GenSeg: Domain Adaptation For Target Modality Tumor Segmentation With Annotation-Efficient SupervisionCode0
Investigating certain choices of CNN configurations for brain lesion segmentation—0
Mind the Gap: Scanner-induced domain shifts pose challenges for representation learning in histopathology—0
DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans—0
Encoding feature supervised UNet++: Redesigning Supervision for liver and tumor segmentation—0
Improved HER2 Tumor Segmentation with Subtype Balancing using Deep Generative Networks—0
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