RanMerFormer: Randomized vision transformer with token merging for brain tumor classification

Jian Wang, Si Yuan Lu, Shui Hua Wang, Yu Dong Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Brains are the control center of the nervous system in human bodies, and brain tumor is one of the most deadly diseases. Currently, magnetic resonance imaging (MRI) is the most effective way to brain tumors early detection in clinical diagnoses due to its superior imaging quality for soft tissues. Manual analysis of brain MRI is error-prone which depends on empirical experience and the fatigue state of the radiologists to a large extent. Computer-aided diagnosis (CAD) systems are becoming more and more impactful because they can provide accurate prediction results based on medical images with advanced techniques from computer vision. Therefore, a novel CAD method for brain tumor classification named RanMerFormer is presented in this paper. A pre-trained vision transformer is used as the backbone model. Then, a merging mechanism is proposed to remove the redundant tokens in the vision transformer, which improves computing efficiency substantially. Finally, a randomized vector functional-link serves as the head in the proposed RanMerFormer, which can be trained swiftly. All the simulation results are obtained from two public benchmark datasets, which reveal that the proposed RanMerFormer can achieve state-of-the-art performance for brain tumor classification. The trained RanMerFormer can be applied in real-world scenarios to assist in brain tumor diagnosis.

Original languageEnglish
Article number127216
JournalNeurocomputing
Volume573
DOIs
Publication statusPublished - 7 Mar 2024

Keywords

  • Brain tumor
  • Computer-aided diagnosis
  • Magnetic resonance image
  • Randomized vector functional-link
  • Token merging
  • Vision transformer

Fingerprint

Dive into the research topics of 'RanMerFormer: Randomized vision transformer with token merging for brain tumor classification'. Together they form a unique fingerprint.

Cite this