Summary of Moab: Multi-modal Outer Arithmetic Block For Fusion Of Histopathological Images and Genetic Data For Brain Tumor Grading, by Omnia Alwazzan (1 and 2) et al.
MOAB: Multi-Modal Outer Arithmetic Block For Fusion Of Histopathological Images And Genetic Data For Brain Tumor Grading
by Omnia Alwazzan, Abbas Khan, Ioannis Patras, Gregory Slabaugh
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel computer-aided diagnosis approach is proposed to accurately predict brain tumor grades based on histological images and genetic data. The Multi-modal Outer Arithmetic Block (MOAB) combines latent representations of both modalities using arithmetic operations, enabling improved prediction of Grade 2, 3, and 4 gliomas. MOAB is evaluated on the TCGA glioma dataset, demonstrating enhanced separation between similar classes (Grade 2 and 3) and outperforming state-of-the-art grade classification techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make it easier for doctors to diagnose brain tumors by combining information from two different types of tests: pictures of the tumor’s cells and genetic data. The new approach, called MOAB, uses special math operations to combine this information, which helps improve accuracy when predicting the grade of a brain tumor (how aggressive it is). By using MOAB on a large dataset of glioma tumors, the researchers were able to make better predictions than other methods that tried to do the same thing. |
Keywords
» Artificial intelligence » Classification » Multi modal
