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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 361–370 of 786 papers

TitleStatusHype
Encoding feature supervised UNet++: Redesigning Supervision for liver and tumor segmentation—0
DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans—0
Learning from partially labeled data for multi-organ and tumor segmentationCode1
Improved HER2 Tumor Segmentation with Subtype Balancing using Deep Generative Networks—0
Generative Adversarial Networks for Weakly Supervised Generation and Evaluation of Brain Tumor Segmentations on MR Images—0
Radiomics-enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck CancerCode1
ESKNet-An enhanced adaptive selection kernel convolution for breast tumors segmentationCode1
ISA-Net: Improved spatial attention network for PET-CT tumor segmentation—0
Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images—0
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic ModelCode3
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