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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

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Showing 231–240 of 436 papers

TitleStatusHype
FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures For Medical Imaging SegmentationCode0
Unsupervised Brain Tumor Segmentation with Image-based Prompts—0
Medical Image Analysis using Deep Relational Learning—0
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging—0
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
Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS 2022 Challenge Solution—0
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