Loughborough University
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Multi-modal hybrid encoding approach based on information bottleneck for brain tumor grading

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journal contribution
posted on 2025-05-06, 10:17 authored by Luyue Yu, Chengyuan LiuChengyuan Liu, Aixi Qu, Qiang Wu, Ju Liu
Grade classification of gliomas is critical in clinical diagnosis and treatment decisions. Although histological images are commonly used for grading and as an important factor in prognostic prediction, their results are prone to inter-observer variability. Recent advancements in molecular genetics have significantly improved tumor classification, but challenges persist in effective feature selection and multi-modal data fusion. This letter proposes a multi-modal hybrid encoding method based on information bottleneck (MHEIB), combining histological images and genetic data to enhance glioma grading. MHEIB effectively fuses multi-modal features through the information bottleneck module and the self-attention mechanism, which compresses and filters the key features and dynamically adjusts the weights of multi-modal features to improve the classification accuracy. Experimental results on The Cancer Genome Atlas (TCGA) glioma dataset demonstrate that MHEIB outperforms several fusion methods in terms of F1-score, AUC, and AP. In particular, MHEIB significantly improved the classification AUC to 89.3% and 83.7% for similar categories of Grades II and III respectively. Overall, the MHEIB method provides an efficient multi-modal data fusion solution for glioma grading.

Funding

10.13039/501100007129 - Natural Science Foundation of Shandong Province (Grant Number: ZR2021MF056 and ZR2023ZD14)

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Published in

IEEE Signal Processing Letters

Volume

32

Pages

651 - 655

Publisher

Institute of Electrical and Electronics Engineers

Version

  • AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

This accepted manuscript has been made available under the Creative Commons Attribution licence (CC BY) under the IEEE JISC UK green open access agreement.

Acceptance date

2025-01-05

Publication date

2025-01-13

Copyright date

2025

ISSN

1070-9908

eISSN

1558-2361

Language

  • en

Depositor

Dr Chengyuan Liu. Deposit date: 2 April 2025