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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 1–25 of 786 papers

TitleStatusHype
3D TransUNet: Advancing Medical Image Segmentation through Vision TransformersCode4
UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image SegmentationCode3
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic ModelCode3
MA-Net: A Multi-Scale Attention Network for Liver and Tumor SegmentationCode3
The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)Code2
Label-Free Liver Tumor SegmentationCode2
FreeTumor: Advance Tumor Segmentation via Large-Scale Tumor SynthesisCode2
Synthetic Tumors Make AI Segment Tumors BetterCode2
U-Mamba: Enhancing Long-range Dependency for Biomedical Image SegmentationCode2
TransBTSV2: Towards Better and More Efficient Volumetric Segmentation of Medical ImagesCode2
Vision Foundation Models for Computed TomographyCode2
LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body ImagingCode2
Conditional Diffusion Models for Semantic 3D Brain MRI SynthesisCode2
3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentationCode2
BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysisCode2
Cross-Modal Interactive Perception Network with Mamba for Lung Tumor Segmentation in PET-CT ImagesCode2
A Robust Volumetric Transformer for Accurate 3D Tumor SegmentationCode1
AutoPET Challenge 2023: Sliding Window-based Optimization of U-NetCode1
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor SegmentationCode1
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor SegmentationCode1
ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly SegmentationCode1
Attention-Guided Version of 2D UNet for Automatic Brain Tumor SegmentationCode1
Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT ImagesCode1
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing ModalitiesCode1
BrainSegFounder: Towards 3D Foundation Models for Neuroimage SegmentationCode1
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