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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 71–80 of 786 papers

TitleStatusHype
3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce RegimesCode1
CAT: Coordinating Anatomical-Textual Prompts for Multi-Organ and Tumor SegmentationCode1
CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentationCode1
CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation NetworkCode1
Continual Learning for Abdominal Multi-Organ and Tumor SegmentationCode1
BrainSegFounder: Towards 3D Foundation Models for Neuroimage SegmentationCode1
CT Liver Segmentation via PVT-based Encoding and Refined DecodingCode1
cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image SynthesisCode1
Brain Tumor Segmentation with Deep Neural NetworksCode1
Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT ImagesCode1
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