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 481–490 of 786 papers

TitleStatusHype
3D AGSE-VNet: An Automatic Brain Tumor MRI Data Segmentation Framework—0
MGI: Multimodal Contrastive pre-training of Genomic and Medical Imaging—0
MiM: Mask in Mask Self-Supervised Pre-Training for 3D Medical Image Analysis—0
Mind the Gap: Promoting Missing Modality Brain Tumor Segmentation with Alignment—0
Mind the Gap: Scanner-induced domain shifts pose challenges for representation learning in histopathology—0
When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation—0
Automated Tumor Segmentation and Brain Mapping for the Tumor Area—0
Automated Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy from DWI Data—0
Artificial Intelligence Model for Tumoral Clinical Decision Support Systems—0
Modality-Aware and Shift Mixer for Multi-modal Brain Tumor Segmentation—0
Show:102550
← PrevPage 49 of 79Next →

No leaderboard results yet.