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Related Concept Videos

Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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Related Experiment Video

Updated: Jun 8, 2026

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
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Artificial Intelligence-Based Methods for Integrating Local and Global Features for Brain Cancer Imaging: Scoping

Hazrat Ali1, Rizwan Qureshi2, Zubair Shah1

  • 1College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.

JMIR Medical Informatics
|November 17, 2023
PubMed
Summary
This summary is machine-generated.

Vision transformers (ViTs) show promise in brain cancer imaging, particularly for tumor segmentation. However, their computational complexity presents a challenge for widespread clinical adoption.

Keywords:
AIartificial intelligencebrain cancerbrain tumormedical imagingsegmentationvision transformers

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Neuroscience

Background:

  • Transformer-based models are increasingly utilized in medical and cancer imaging.
  • Recent studies highlight their application in brain cancer diagnosis and tumor segmentation.

Purpose of the Study:

  • To review the contribution of various vision transformers (ViTs) to brain cancer diagnosis and segmentation.
  • To examine ViT architectures for enhancing brain tumor segmentation.
  • To explore how ViT models improve convolutional neural network performance in brain cancer imaging.

Main Methods:

  • Systematic review following PRISMA-ScR guidelines.
  • Searches conducted on PubMed, Scopus, IEEE Xplore, and Google Scholar.
  • Independent reviewer selection and data extraction, with narrative synthesis.

Main Results:

  • 22 studies (2021-2022) were included, focusing primarily on tumor segmentation using ViTs.
  • Shifted Window transformer architectures are currently most popular.
  • UNet transformer and TransUNet architectures require significant computational resources (8 GPUs).
  • ViTs are combined with CNNs to capture both global and local image contexts.

Conclusions:

  • Computational complexity of transformer architectures is a significant bottleneck for clinical translation.
  • This review offers valuable insights for researchers in medical AI and brain cancer applications.