SOTAVerified

Brain Tumor Segmentation

Brain Tumor Segmentation is a medical image analysis task that involves the separation of brain tumors from normal brain tissue in magnetic resonance imaging (MRI) scans. The goal of brain tumor segmentation is to produce a binary or multi-class segmentation map that accurately reflects the location and extent of the tumor.

( Image credit: Brain Tumor Segmentation with Deep Neural Networks )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 171–180 of 436 papers

TitleStatusHype
M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing ModalitiesCode1
Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging—0
Multi-class Brain Tumor Segmentation using Graph Attention Network—0
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation—0
Exploiting Partial Common Information Microstructure for Multi-Modal Brain Tumor SegmentationCode0
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization—0
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
Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS 2022 Challenge Solution—0
Towards fully automated deep-learning-based brain tumor segmentation: is brain extraction still necessary?Code0
Show:102550
← PrevPage 18 of 44Next →

No leaderboard results yet.