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Related Experiment Video

Updated: Sep 27, 2025

Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma

Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma

Published on: September 13, 2022

A weakly supervised deep learning-based method for glioma subtype classification using WSI and mpMRIs.

Wei-Wen Hsu¹, Jing-Ming Guo¹, Linmin Pei²

¹Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, ROC.

Scientific Reports

|April 13, 2022

Summary

This summary is machine-generated.

This study introduces a hybrid deep learning method for glioma subtype classification using whole slide imaging and multiparametric MRI. Combining both modalities improves accuracy, overcoming challenges in weakly supervised learning.

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Area of Science:

* Computational pathology and medical imaging analysis.
* Artificial intelligence in neuro-oncology.
* Deep learning for medical image classification.

Background:

* Accurate glioma subtype classification is crucial for effective brain tumor treatment.
* Computer-aided classification algorithms face challenges, including label constraints without precise lesion region information.
* Integrating multi-modal data (histopathology and MRI) offers potential for improved diagnostic accuracy.

Purpose of the Study:

* To develop a novel hybrid fully convolutional neural network (CNN)-based method for glioma subtype classification.
* To address the challenge of label constraints in histopathology data using a weakly supervised approach.
* To enhance classification robustness by fusing information from whole slide imaging (WSI) and multiparametric magnetic resonance imaging (mpMRI).

Main Methods:

* A 2D CNN was employed for glioma subtype classification on WSIs, utilizing a weakly supervised method to extract representative patches.
* A 3D CNN-based approach was developed for mpMRI analysis, including brain tumor segmentation and classification.
* A confidence index guided the fusion of WSI-based and mpMRI-based results to improve prediction robustness.

Main Results:

* The hybrid method, integrating both WSI and mpMRI data, demonstrated superior performance compared to using either modality alone.
* The proposed method achieved third place in the CPM-RadPath 2020 competition's testing phase.
* Experimental results on the CPM-RadPath 2020 validation dataset confirmed the comprehensive judgments from multi-modality data.

Conclusions:

* The developed hybrid CNN-based method effectively classifies glioma subtypes by leveraging multi-modality data.
* The weakly supervised approach successfully mitigated label constraint issues in WSI analysis.
* Fusion of WSI and mpMRI data, guided by a confidence index, enhances classification performance and robustness.