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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
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
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic ModelCode3
FreeTumor: Advance Tumor Segmentation via Large-Scale Tumor SynthesisCode2
Conditional Diffusion Models for Semantic 3D Brain MRI 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
Vision Foundation Models for Computed TomographyCode2
TransBTSV2: Towards Better and More Efficient Volumetric Segmentation of Medical ImagesCode2
The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)Code2
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
Label-Free Liver Tumor SegmentationCode2
LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body ImagingCode2
Synthetic Tumors Make AI Segment Tumors BetterCode2
DeepSeg: Deep Neural Network Framework for Automatic Brain Tumor Segmentation using Magnetic Resonance FLAIR ImagesCode1
AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT ImagesCode1
Demystifying Brain Tumour Segmentation Networks: Interpretability and Uncertainty AnalysisCode1
DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing ModalitiesCode1
Continual Learning for Abdominal Multi-Organ and Tumor SegmentationCode1
CT Liver Segmentation via PVT-based Encoding and Refined DecodingCode1
Deep Learning Based Brain Tumor Segmentation: A SurveyCode1
CSC-PA: Cross-image Semantic Correlation via Prototype Attentions for Single-network Semi-supervised Breast Tumor SegmentationCode1
cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image SynthesisCode1
BUSIS: A Benchmark for Breast Ultrasound Image SegmentationCode1
Abstracting Deep Neural Networks into Concept Graphs for Concept Level InterpretabilityCode1
Deep Learning-Based Concurrent Brain Registration and Tumor SegmentationCode1
DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CTCode1
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing ModalitiesCode1
Diffusion Models for Implicit Image Segmentation EnsemblesCode1
CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI SynthesisCode1
CAT: Coordinating Anatomical-Textual Prompts for Multi-Organ and Tumor SegmentationCode1
CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentationCode1
Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion modelsCode1
Brain Tumor Segmentation and Survival Prediction using 3D Attention UNetCode1
Brain Tumor Segmentation with Deep Neural NetworksCode1
CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation NetworkCode1
A Robust Volumetric Transformer for Accurate 3D Tumor SegmentationCode1
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain TumorCode1
Adding Seemingly Uninformative Labels Helps in Low Data RegimesCode1
3D Self-Supervised Methods for Medical ImagingCode1
AutoPET Challenge 2023: Sliding Window-based Optimization of U-NetCode1
CANet: Context Aware Network for 3D Brain Glioma SegmentationCode1
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor SegmentationCode1
Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion ModelsCode1
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor SegmentationCode1
Annotation-efficient deep learning for automatic medical image segmentationCode1
MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic SegmentationCode1
Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT ImagesCode1
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