IMAN: An Adaptive Network for Robust NPC Mortality Prediction with Missing Modalities

TL;DR

Compared to existing methods, IMAN consistently outperforms in scenarios with incomplete data, representing a significant advancement in mortality prediction for medical diagnostics and treatment planning.

Abstract

Accurate prediction of mortality in nasopharyngeal carcinoma (NPC), a complex malignancy particularly challenging in advanced stages, is crucial for optimizing treatment strategies and improving patient outcomes. However, this predictive process is often compromised by the high-dimensional and heterogeneous nature of NPC-related data, coupled with the pervasive issue of incomplete multi-modal data, manifesting as missing radiological images or incomplete diagnostic reports. Traditional machine learning approaches suffer significant performance degradation when faced with such incomplete data, as they fail to effectively handle the high-dimensionality and intricate correlations across modalities. Even advanced multi-modal learning techniques like Transformers struggle to maintain robust performance in the presence of missing modalities, as they lack specialized mechanisms to adaptively integrate and align the diverse data types, while also capturing nuanced patterns and contextual relationships within the complex NPC data. To address these problem, we introduce IMAN: an adaptive network for robust NPC mortality prediction with missing modalities.

Figures (3)

• Figure 1: Comparison of performance at different levels of missingness.
• Figure 2: The overview of IMAN.
• Figure 3: Comparison of AUC performance of various methods with different missing rates: EBV, "normal", and medical imaging modalities.