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 526–550 of 786 papers

TitleStatusHype
Glioblastoma Multiforme Prognosis: MRI Missing Modality Generation, Segmentation and Radiogenomic Survival PredictionCode0
TransMed: Transformers Advance Multi-modal Medical Image Classification—0
Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentation—0
TransBTS: Multimodal Brain Tumor Segmentation Using TransformerCode1
ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly SegmentationCode1
PA-ResSeg: A Phase Attention Residual Network for Liver Tumor Segmentation from Multi-phase CT Images—0
Representation Disentanglement for Multi-modal brain MR AnalysisCode1
Post-hoc Overall Survival Time Prediction from Brain MRICode0
Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT ImagesCode1
Benefits of Linear Conditioning with Metadata for Image Segmentation—0
NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentationCode1
Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation—0
3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint—0
Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Review—0
Dosimetric impact of physician style variations in contouring CTV for post-operative prostate cancer: A deep learning-based simulation study—0
Belief function-based semi-supervised learning for brain tumor segmentation—0
Multi-Threshold Attention U-Net (MTAU) based Model for Multimodal Brain Tumor Segmentation in MRI scans—0
A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction—0
Glioblastoma Multiforme Patient Survival Prediction—0
Automatic Liver Segmentation from CT Images Using Deep Learning Algorithms: A Comparative StudyCode0
Expectation-Maximization Regularized Deep Learning for Weakly Supervised Tumor Segmentation for Glioblastoma—0
Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology—0
A Unified Conditional Disentanglement Framework for Multimodal Brain MR Image Translation—0
Brain Tumor Segmentation and Survival Prediction using Automatic Hard mining in 3D CNN Architecture—0
Multi-stage Deep Layer Aggregation for Brain Tumor Segmentation—0
Show:102550
← PrevPage 22 of 32Next →

No leaderboard results yet.