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 776–786 of 786 papers

TitleStatusHype
Efficient Parameter Adaptation for Multi-Modal Medical Image Segmentation and Prognosis—0
Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation—0
Brain Tumor Segmentation on MRI with Missing Modalities—0
Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation—0
Encoding feature supervised UNet++: Redesigning Supervision for liver and tumor segmentation—0
End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks—0
End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation—0
Enhancing Brain Tumor Classification Using TrAdaBoost and Multi-Classifier Deep Learning Approaches—0
AMM-Diff: Adaptive Multi-Modality Diffusion Network for Missing Modality Imputation—0
All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation—0
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation—0
Show:102550
← PrevPage 32 of 32Next →

No leaderboard results yet.