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Related Experiment Video

Updated: Aug 23, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Multi-modal Learning with Missing Data for Cancer Diagnosis Using Histopathological and Genomic Data.

Can Cui¹, Zuhayr Asad¹, William F Dean²

¹Department of Computer Science, Vanderbilt University, Nashville, TN 37235, USA.

Proceedings of Spie--The International Society for Optical Engineering

|October 28, 2022

Summary

This study introduces a multi-modal learning model that effectively uses incomplete patient data for cancer diagnosis. The approach improves glioma cancer grade classification by integrating histological images and genomic data, even with missing information.

Keywords:

Multi-modal learning deep learning missing data

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Area of Science:

Oncology
Bioinformatics
Computational Pathology

Background:

Multi-modal learning, integrating pathological images and genomic data, enhances cancer diagnosis and prognosis compared to single-modality approaches.
Missing data in clinical practice is a significant challenge, with existing methods often discarding incomplete patient samples, leading to information loss and potential overfitting.

Purpose of the Study:

To develop and evaluate a generalized multi-modal learning framework capable of handling missing data for cancer diagnosis.
To integrate histological images and genomic data effectively, utilizing all available patient information, regardless of data completeness.

Main Methods:

Developed an integrated model for multi-modal learning that accommodates missing histological images or genomic data.
Applied the model to public TCGA-GBM and TCGA-LGG datasets for glioma cancer grade classification.

Main Results:

The integrated model successfully utilized data from patients with both complete and partial modalities.
Incorporating data from samples with missing modalities improved the overall model performance in glioma cancer grade classification.

Conclusions:

Multi-modal learning models can be effectively adapted to handle missing data in clinical settings.
Utilizing all available data, including incomplete samples, enhances the accuracy and robustness of cancer diagnosis and prognosis models.