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
CTVR-EHO TDA-IPH Topological Optimized Convolutional Visual Recurrent Network for Brain Tumor Segmentation and Classification—0
Topology-Aware Focal Loss for 3D Image Segmentation—0
Towards a Multimodal MRI-Based Foundation Model for Multi-Level Feature Exploration in Segmentation, Molecular Subtyping, and Grading of Glioma—0
Towards SAMBA: Segment Anything Model for Brain Tumor Segmentation in Sub-Sharan African Populations—0
Brain MRI Tumor Segmentation with Adversarial Networks—0
Transfer learning for automatic brain tumor classification Using MRI Images.—0
Transfer Learning for Brain Tumor Segmentation—0
Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Review—0
Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation—0
TransMed: Transformers Advance Multi-modal Medical Image Classification—0
Treatment-aware Diffusion Probabilistic Model for Longitudinal MRI Generation and Diffuse Glioma Growth Prediction—0
Quantifying uncertainty in lung cancer segmentation with foundation models applied to mixed-domain datasets—0
Trustworthy Multi-phase Liver Tumor Segmentation via Evidence-based Uncertainty—0
Tumor-Centered Patching for Enhanced Medical Image Segmentation—0
Improving Deep Learning Models for Pediatric Low-Grade Glioma Tumors Molecular Subtype Identification Using 3D Probability Distributions of Tumor Location—0
Tumor Location-weighted MRI-Report Contrastive Learning: A Framework for Improving the Explainability of Pediatric Brain Tumor Diagnosis—0
Tumor segmentation on whole slide images: training or prompting?—0
TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks—0
Two-Stage Approach for Brain MR Image Synthesis: 2D Image Synthesis and 3D Refinement—0
Two-stage MR Image Segmentation Method for Brain Tumors based on Attention Mechanism—0
Two Stage Segmentation of Cervical Tumors using PocketNet—0
Uncertainty-driven refinement of tumor-core segmentation using 3D-to-2D networks with label uncertainty—0
Uncertainty-Guided Coarse-to-Fine Tumor Segmentation with Anatomy-Aware Post-Processing—0
Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation—0
Unified HT-CNNs Architecture: Transfer Learning for Segmenting Diverse Brain Tumors in MRI from Gliomas to Pediatric Tumors—0
Show:102550
← PrevPage 22 of 32Next →

No leaderboard results yet.