MOAB: Multi-Modal Outer Arithmetic Block For Fusion Of Histopathological Images And Genetic Data For Brain Tumor Grading
<|reference_start|>MOAB: Multi-Modal Outer Arithmetic Block For Fusion Of Histopathological Images And Genetic Data For Brain Tumor Grading: Brain tumors are an abnormal growth of cells in the brain. They can be classified into distinct grades based on their growth. Often grading is performed based on a histological image and is one of the most significant predictors of a patients prognosis, the higher the grade, the more aggressive the tumor. Correct diagnosis of a tumor grade remains challenging. Though histopathological grading has been shown to be prognostic, results are subject to interobserver variability, even among experienced pathologists. Recently, the World Health Organization reported that advances in molecular genetics have led to improvements in tumor classification. This paper seeks to integrate histological images and genetic data for improved computer-aided diagnosis. We propose a novel Multi-modal Outer Arithmetic Block (MOAB) based on arithmetic operations to combine latent representations of the different modalities for predicting the tumor grade (Grade \rom{2}, \rom{3} and \rom{4}). Extensive experiments evaluate the effectiveness of our approach. By applying MOAB to The Cancer Genome Atlas (TCGA) glioma dataset, we show that it can improve separation between similar classes (Grade \rom{2} and \rom{3}) and outperform prior state-of-the-art grade classification techniques.<|reference_end|>
@article{alwazzan2024moab:,
    title={MOAB: Multi-Modal Outer Arithmetic Block For Fusion Of Histopathological
  Images And Genetic Data For Brain Tumor Grading},
    author={Omnia Alwazzan (1 and 2), Abbas Khan (1 and 2), Ioannis Patras (1 and
  2), Gregory Slabaugh (1 and 2) ((1) School of Electronic Engineering and
  Computer Science, Queen Mary University of London, UK, (2) Queen Mary Digital
  Environment Research Institute (DERI), London, UK)},
    journal={pages={1--5},year={2023},organization={IEEE}},
    year={2024},
    doi={10.1109/ISBI53787.2023.10230698},
    archivePrefix={arXiv},
    eprint={2403.06349},
    primaryClass={cs.CV cs.AI}
}