Multi-FRuGaL: Multimodal Flexible Redundancy-aware Decomposed Gated Learning for Cancer Diagnosis and Prognosis

2026-06-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Medical Imaging & Diagnostics · Depth: Expert, extended

Summary

The Multi-FRuGaL framework addresses challenges in multimodal cancer diagnosis and prognosis, particularly with incomplete and redundant patient data. This adaptive gated intermediate-fusion framework integrates per-modality encoders with a signal decomposition layer, an input-conditioned gating network, and an information-aware fusion objective. It separates redundant from modality-specific complementary signals, selectively upweighting informative modalities and suppressing noisy inputs, even when multiple modalities are absent. Evaluated on two head and neck cancer cohorts, HANCOCK ($N=763$) and HECKTOR ($N=588$), Multi-FRuGaL consistently outperformed baselines. It improved AUC from 0.601 to 0.8496 for survival and from 0.672 to 0.8102 for recurrence on HANCOCK, achieving 0.975 AUC for HPV prediction on HECKTOR. The framework also demonstrated robustness under up to 60% missing-modality conditions.

Key takeaway

For AI Scientists and Machine Learning Engineers developing clinical prediction models, Multi-FRuGaL offers a robust approach to handling real-world multimodal data challenges. You should consider implementing its signal decomposition and adaptive gating mechanisms to manage missingness and redundancy effectively. This can lead to more accurate and interpretable prognostic models, particularly in oncology, by ensuring that only the most complementary information drives predictions.

Key insights

Adaptive, redundancy-aware gated fusion improves cancer prognosis with incomplete multimodal patient data.

Principles

• Decompose signals into shared and specific components.
• Dynamically gate modality contributions based on informativeness.
• Penalize redundancy and encourage sparse modality usage.

Method

Multi-FRuGaL uses signal decomposition, gated tokenization, a masked fusion encoder, and a prediction head, trained with task, decomposition, and information-budget losses.

In practice

• Apply Gumbel-Sigmoid relaxation for soft gate values.
• Use orthogonality constraints to separate signal components.
• Implement budget and redundancy penalties for efficient fusion.

Topics

• Multimodal Fusion
• Cancer Prognosis
• Missing Data Handling
• Gated Learning
• Signal Decomposition
• Head and Neck Cancer

Code references

• IUCompPath/Multi_Frugal

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.