SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 501–550 of 786 papers

TitleStatusHype
Transfer learning for automatic brain tumor classification Using MRI Images.—0
Transfer Learning for Brain Tumor Segmentation—0
Multi-class Brain Tumor Segmentation using Graph Attention Network—0
Multiclass MRI Brain Tumor Segmentation using 3D Attention-based U-Net—0
Multiclass Spinal Cord Tumor Segmentation on MRI with Deep Learning—0
Multi-Decoder Networks with Multi-Denoising Inputs for Tumor Segmentation—0
Multi-Domain Image Completion for Random Missing Input Data—0
Multi-encoder nnU-Net outperforms Transformer models with self-supervised pretraining—0
Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation—0
Multi-Layer Feature Fusion with Cross-Channel Attention-Based U-Net for Kidney Tumor Segmentation—0
Automated ensemble method for pediatric brain tumor segmentation—0
Multimodal 3D Brain Tumor Segmentation with Adversarial Training and Conditional Random Field—0
Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusion—0
Multi-Modal Brain Tumor Segmentation via 3D Multi-Scale Self-attention and Cross-attention—0
Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS 2022 Challenge Solution—0
Multi Modal Convolutional Neural Networks for Brain Tumor Segmentation—0
Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Review—0
Automated Ensemble-Based Segmentation of Adult Brain Tumors: A Novel Approach Using the BraTS AFRICA Challenge Data—0
Multimodal Learning With Intraoperative CBCT & Variably Aligned Preoperative CT Data To Improve Segmentation—0
Multimodal MRI brain tumor segmentation using random forests with features learned from fully convolutional neural network—0
Multimodal Self-Supervised Learning for Medical Image Analysis—0
Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentation—0
Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting—0
Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation—0
Multi-Resolution 3D CNN for MRI Brain Tumor Segmentation and Survival Prediction—0
Automated 3D Tumor Segmentation using Temporal Cubic PatchGAN (TCuP-GAN)—0
Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation—0
Multi Scale Supervised 3D U-Net for Kidney and Tumor Segmentation—0
Automated 3D Segmentation of Kidneys and Tumors in MICCAI KiTS 2023 Challenge—0
A Cascaded Deep-Learning Framework for Segmentation of Metastatic Brain Tumors Before and After Stereotactic Radiation Therapy—0
Multi-Slice Dense-Sparse Learning for Efficient Liver and Tumor Segmentation—0
Multi-stage Deep Layer Aggregation for Brain Tumor Segmentation—0
TransMed: Transformers Advance Multi-modal Medical Image Classification—0
Multi-Task Generative Adversarial Network for Handling Imbalanced Clinical Data—0
Multi-task Learning To Improve Semantic Segmentation Of CBCT Scans Using Image Reconstruction—0
Multi-Threshold Attention U-Net (MTAU) based Model for Multimodal Brain Tumor Segmentation in MRI scans—0
A Unified Conditional Disentanglement Framework for Multimodal Brain MR Image Translation—0
Attention Xception UNet (AXUNet): A Novel Combination of CNN and Self-Attention for Brain Tumor Segmentation—0
Treatment-aware Diffusion Probabilistic Model for Longitudinal MRI Generation and Diffuse Glioma Growth Prediction—0
A Tri-attention Fusion Guided Multi-modal Segmentation Network—0
A Transformer-based Generative Adversarial Network for Brain Tumor Segmentation—0
A CADe System for Gliomas in Brain MRI using Convolutional Neural Networks—0
Neural Network-Based Automatic Liver Tumor Segmentation With Random Forest-Based Candidate Filtering—0
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging—0
Non Parametric Data Augmentations Improve Deep-Learning based Brain Tumor Segmentation—0
A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction—0
A Structural Graph-Based Method for MRI Analysis—0
ASLseg: Adapting SAM in the Loop for Semi-supervised Liver Tumor Segmentation—0
ONCOPILOT: A Promptable CT Foundation Model For Solid Tumor Evaluation—0
Quantifying uncertainty in lung cancer segmentation with foundation models applied to mixed-domain datasets—0
Show:102550
← PrevPage 11 of 16Next →

No leaderboard results yet.