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 351–375 of 786 papers

TitleStatusHype
PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image AnalysisCode1
Scratch Each Other's Back: Incomplete Multi-Modal Brain Tumor Segmentation via Category Aware Group Self-Support LearningCode1
Investigation of Network Architecture for Multimodal Head-and-Neck Tumor Segmentation—0
Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS 2022 Challenge Solution—0
Robust Learning Protocol for Federated Tumor Segmentation Challenge—0
Towards fully automated deep-learning-based brain tumor segmentation: is brain extraction still necessary?Code0
M-GenSeg: Domain Adaptation For Target Modality Tumor Segmentation With Annotation-Efficient SupervisionCode0
Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive LearningCode1
Investigating certain choices of CNN configurations for brain lesion segmentation—0
Mind the Gap: Scanner-induced domain shifts pose challenges for representation learning in histopathology—0
Encoding feature supervised UNet++: Redesigning Supervision for liver and tumor segmentation—0
DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans—0
Learning from partially labeled data for multi-organ and tumor segmentationCode1
Improved HER2 Tumor Segmentation with Subtype Balancing using Deep Generative Networks—0
Generative Adversarial Networks for Weakly Supervised Generation and Evaluation of Brain Tumor Segmentations on MR Images—0
Radiomics-enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck CancerCode1
ESKNet-An enhanced adaptive selection kernel convolution for breast tumors segmentationCode1
ISA-Net: Improved spatial attention network for PET-CT tumor segmentation—0
Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images—0
Exploring Structure-Wise Uncertainty for 3D Medical Image Segmentation—0
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic ModelCode3
Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation—0
Hyper-Connected Transformer Network for Multi-Modality PET-CT Segmentation—0
Synthetic Tumors Make AI Segment Tumors BetterCode2
CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation NetworkCode1
Show:102550
← PrevPage 15 of 32Next →

No leaderboard results yet.