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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 301–350 of 786 papers

TitleStatusHype
Continual Learning for Abdominal Multi-Organ and Tumor SegmentationCode1
Diagnosis and Prognosis of Head and Neck Cancer Patients using Artificial Intelligence—0
Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI—0
The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)—0
Conditional Diffusion Models for Semantic 3D Brain MRI SynthesisCode2
propnet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors on CT Scans—0
The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)Code2
BreastSAM: A Study of Segment Anything Model for Breast Tumor Detection in Ultrasound Images—0
AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT ImagesCode1
The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)—0
The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via InpaintingCode1
Learning to Learn Unlearned Feature for Brain Tumor Segmentation—0
Squeeze Excitation Embedded Attention UNet for Brain Tumor Segmentation—0
The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma—0
Multiclass MRI Brain Tumor Segmentation using 3D Attention-based U-Net—0
Trustworthy Multi-phase Liver Tumor Segmentation via Evidence-based Uncertainty—0
Self-Supervised Learning for Organs At Risk and Tumor Segmentation with Uncertainty Quantification—0
Flexible Fusion Network for Multi-modal Brain Tumor Segmentation—0
Brain Tumor Segmentation from MRI Images using Deep Learning Techniques—0
Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reportingCode1
3D Brainformer: 3D Fusion Transformer for Brain Tumor Segmentation—0
FedPIDAvg: A PID controller inspired aggregation method for Federated Learning—0
Topology-Aware Focal Loss for 3D Image Segmentation—0
When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation—0
Two-stage MR Image Segmentation Method for Brain Tumors based on Attention Mechanism—0
The Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planning—0
Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image SegmentationCode1
Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning—0
Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging—0
FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures For Medical Imaging SegmentationCode0
Cross-modal tumor segmentation using generative blending augmentation and self trainingCode0
Unsupervised Brain Tumor Segmentation with Image-based Prompts—0
Medical Image Analysis using Deep Relational Learning—0
Label-Free Liver Tumor SegmentationCode2
Memory-Efficient 3D Denoising Diffusion Models for Medical Image ProcessingCode1
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging—0
M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing ModalitiesCode1
3D PETCT Tumor Lesion Segmentation via GCN Refinement—0
Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging—0
Multi-class Brain Tumor Segmentation using Graph Attention Network—0
SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Images—0
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation—0
A Generalized Surface Loss for Reducing the Hausdorff Distance in Medical Imaging SegmentationCode0
Exploiting Partial Common Information Microstructure for Multi-Modal Brain Tumor SegmentationCode0
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization—0
Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma SegmentationCode0
Neural Gas Network Image Features and Segmentation for Brain Tumor Detection Using Magnetic Resonance Imaging DataCode0
Spatially Covariant Lesion Segmentation—0
Multi-Scanner Canine Cutaneous Squamous Cell Carcinoma Histopathology DatasetCode0
Artificial Intelligence Model for Tumoral Clinical Decision Support Systems—0
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