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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 1–50 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
CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentationCode1
CSC-PA: Cross-image Semantic Correlation via Prototype Attentions for Single-network Semi-supervised Breast Tumor SegmentationCode1
XLSTM-HVED: Cross-Modal Brain Tumor Segmentation and MRI Reconstruction Method Using Vision XLSTM and Heteromodal Variational Encoder-DecoderCode1
cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image SynthesisCode1
Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and PediatricsCode1
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
3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce RegimesCode1
The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed TomographyCode1
SiNGR: Brain Tumor Segmentation via Signed Normalized Geodesic Transform RegressionCode1
PRISM: A Promptable and Robust Interactive Segmentation Model with Visual PromptsCode1
Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor SegmentationCode1
D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image SegmentationCode1
CT Liver Segmentation via PVT-based Encoding and Refined DecodingCode1
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image SegmentationCode1
ZePT: Zero-Shot Pan-Tumor Segmentation via Query-Disentangling and Self-PromptingCode1
Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple HospitalsCode1
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