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 611–620 of 786 papers

TitleStatusHype
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
Artificial Intelligence Model for Tumoral Clinical Decision Support Systems—0
Modality-Aware and Shift Mixer for Multi-modal Brain Tumor Segmentation—0
Modality-Pairing Learning for Brain Tumor Segmentation—0
Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images—0
MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network—0
MRI brain tumor segmentation using informative feature vectors and kernel dictionary learning—0
Show:102550
← PrevPage 62 of 79Next →

No leaderboard results yet.