  Accurate medical classification requires a large number of multi-modal data,
and in many cases, different feature types. Previous studies have shown
promising results when using multi-modal data, outperforming single-modality
models when classifying diseases such as Alzheimer's Disease (AD). However,
those models are usually not flexible enough to handle missing modalities.
Currently, the most common workaround is discarding samples with missing
modalities which leads to considerable data under-utilization. Adding to the
fact that labeled medical images are already scarce, the performance of
data-driven methods like deep learning can be severely hampered. Therefore, a
multi-modal method that can handle missing data in various clinical settings is
highly desirable. In this paper, we present Multi-Modal Mixing Transformer
(3MAT), a disease classification transformer that not only leverages
multi-modal data but also handles missing data scenarios. In this work, we test
3MT for AD and Cognitively normal (CN) classification and mild cognitive
impairment (MCI) conversion prediction to progressive MCI (pMCI) or stable MCI
(sMCI) using clinical and neuroimaging data. The model uses a novel Cascaded
Modality Transformer architecture with cross-attention to incorporate
multi-modal information for more informed predictions. We propose a novel
modality dropout mechanism to ensure an unprecedented level of modality
independence and robustness to handle missing data scenarios. The result is a
versatile network that enables the mixing of arbitrary numbers of modalities
with different feature types and also ensures full data utilization missing
data scenarios. The model is trained and evaluated on the ADNI dataset with the
SOTRA performance and further evaluated with the AIBL dataset with missing
data.
