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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 31–40 of 786 papers

TitleStatusHype
Multiscale Encoder and Omni-Dimensional Dynamic Convolution Enrichment in nnU-Net for Brain Tumor SegmentationCode1
Fed-MUnet: Multi-modal Federated Unet for Brain Tumor SegmentationCode1
SMAFormer: Synergistic Multi-Attention Transformer for Medical Image SegmentationCode1
Prototype Learning Guided Hybrid Network for Breast Tumor Segmentation in DCE-MRICode1
Embracing Massive Medical DataCode1
SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text CuesCode1
Unsupervised Domain Adaptation for Pediatric Brain Tumor SegmentationCode1
CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI SynthesisCode1
BrainSegFounder: Towards 3D Foundation Models for Neuroimage SegmentationCode1
CAT: Coordinating Anatomical-Textual Prompts for Multi-Organ and Tumor SegmentationCode1
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