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Constructing and Visualizing Models using Mime-based Machine-learning Framework

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ProtoMM: Interpretable Prototype-Based Multimodal Model for Brain Cancer Survival Prediction.

Yuancheng Yang^1,2, Renkai Ying^1,2, Chao Tong^3,4

¹School of Computer Science and Engineering, Beihang University, Beijing, China.

Journal of Imaging Informatics in Medicine

|October 31, 2025

Summary

We introduce ProtoMM, an interpretable deep learning model for multimodal medical data analysis. This prototype-based approach enhances trust and reliability in medical diagnosis by providing transparent explanations for predictions.

Keywords:

Multimodal learning Prototype-based interpretability Survival prediction

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Area of Science:

Artificial Intelligence in Medicine
Medical Data Analysis
Deep Learning for Healthcare

Background:

Deep learning significantly advances medical imaging analysis.
Multimodal data integration is crucial for medical diagnosis.
Existing deep learning models often lack interpretability in multimodal settings, hindering trust and reliability.

Purpose of the Study:

To develop an interpretable multimodal deep learning model for medical data analysis.
To address the limitations of black-box models in critical medical decision-making.
To enhance trust and reliability through transparent inference processes.

Main Methods:

Proposed ProtoMM, a prototype-based multimodal model emphasizing interpretability.
Utilized self-explanatory prototypes and transparent inference for reliable case explanations.
Employed multimodal fusion with two prototype layer levels: aggregate and singleton layers.

Main Results:

ProtoMM achieved a Concordance Index (C-Index) of 0.793 ± 0.027 in survival prediction tasks.
Performance is comparable to state-of-the-art black-box models.
The model provides fully interpretable insights into its decision-making process.

Conclusions:

ProtoMM offers a reliable and interpretable solution for multimodal medical data analysis.
The prototype-based approach enhances understanding and trust in AI-driven medical decisions.
This model represents a significant step towards explainable AI in clinical applications.