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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 381–390 of 786 papers

TitleStatusHype
Image-level supervision and self-training for transformer-based cross-modality tumor segmentation—0
Segment Anything Model for Brain Tumor Segmentation—0
A Localization-to-Segmentation Framework for Automatic Tumor Segmentation in Whole-Body PET/CT ImagesCode0
Treatment-aware Diffusion Probabilistic Model for Longitudinal MRI Generation and Diffuse Glioma Growth Prediction—0
Towards Optimal Patch Size in Vision Transformers for Tumor SegmentationCode0
Cheap Lunch for Medical Image Segmentation by Fine-tuning SAM on Few Exemplars—0
Tumor-Centered Patching for Enhanced Medical Image Segmentation—0
Anisotropic Hybrid Networks for liver tumor segmentation with uncertainty quantification—0
CARE: A Large Scale CT Image Dataset and Clinical Applicable Benchmark Model for Rectal Cancer Segmentation—0
DSFNet: Dual-GCN and Location-fused Self-attention with Weighted Fast Normalized Fusion for Polyps SegmentationCode0
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