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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 211–220 of 786 papers

TitleStatusHype
Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation on multi-parametric MRI—0
SEDNet: Shallow Encoder-Decoder Network for Brain Tumor SegmentationCode0
CT Liver Segmentation via PVT-based Encoding and Refined DecodingCode1
To deform or not: treatment-aware longitudinal registration for breast DCE-MRI during neoadjuvant chemotherapy via unsupervised keypoints detectionCode0
Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain TumorsCode0
Beyond Traditional Approaches: Multi-Task Network for Breast Ultrasound DiagnosisCode0
Fully Automated Tumor Segmentation for Brain MRI data using Multiplanner UNet—0
Decentralized Gossip Mutual Learning (GML) for automatic head and neck tumor segmentation—0
U-Mamba: Enhancing Long-range Dependency for Biomedical Image SegmentationCode2
Complementary Information Mutual Learning for Multimodality Medical Image Segmentation—0
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