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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
MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic SegmentationCode1
Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion modelsCode1
Brain Tumor Segmentation with Deep Neural NetworksCode1
CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation NetworkCode1
A Joint Graph and Image Convolution Network for Automatic Brain Tumor SegmentationCode1
Brain Tumor Segmentation and Survival Prediction using 3D Attention UNetCode1
Adding Seemingly Uninformative Labels Helps in Low Data RegimesCode1
3D Self-Supervised Methods for Medical ImagingCode1
A Robust Volumetric Transformer for Accurate 3D Tumor SegmentationCode1
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
ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly SegmentationCode1
Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT ImagesCode1
Annotation-efficient deep learning for automatic medical image segmentationCode1
AutoPET Challenge 2023: Sliding Window-based Optimization of U-NetCode1
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