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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
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic ModelCode3
UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image SegmentationCode3
MA-Net: A Multi-Scale Attention Network for Liver and Tumor SegmentationCode3
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
Vision Foundation Models for Computed TomographyCode2
LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body ImagingCode2
FreeTumor: Advance Tumor Segmentation via Large-Scale Tumor SynthesisCode2
U-Mamba: Enhancing Long-range Dependency for Biomedical Image SegmentationCode2
3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentationCode2
Conditional Diffusion Models for Semantic 3D Brain MRI SynthesisCode2
The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)Code2
Label-Free Liver Tumor SegmentationCode2
Synthetic Tumors Make AI Segment Tumors BetterCode2
TransBTSV2: Towards Better and More Efficient Volumetric Segmentation of Medical ImagesCode2
TextBraTS: Text-Guided Volumetric Brain Tumor Segmentation with Innovative Dataset Development and Fusion Module ExplorationCode1
Rethinking Brain Tumor Segmentation from the Frequency Domain PerspectiveCode1
DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing ModalitiesCode1
Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion ModelsCode1
PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data GenerationCode1
GBT-SAM: Adapting a Foundational Deep Learning Model for Generalizable Brain Tumor Segmentation via Efficient Integration of Multi-Parametric MRI DataCode1
A Reverse Mamba Attention Network for Pathological Liver SegmentationCode1
Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image SynthesisCode1
Triad: Vision Foundation Model for 3D Magnetic Resonance ImagingCode1
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