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Prostate cancer is one of the leading causes of cancer-related death in men
worldwide. Like many cancers, diagnosis involves expert integration of
heterogeneous patient information such as imaging, clinical risk factors, and
more. For this reason, there have been many recent efforts toward deep
multimodal fusion of image and non-image data for clinical decision tasks. Many
of these studies propose methods to fuse learned features from each patient
modality, providing significant downstream improvements with techniques like
cross-modal attention gating, Kronecker product fusion, orthogonality
regularization, and more. While these enhanced fusion operations can improve
upon feature concatenation, they often come with an extremely high learning
capacity, meaning they are likely to overfit when applied even to small or
low-dimensional datasets. Rather than designing a highly expressive fusion
operation, we propose three simple methods for improved multimodal fusion with
small datasets that aid optimization by generating auxiliary sources of
supervision during training: extra supervision, clinical prediction, and dense
fusion. We validate the proposed approaches on prostate cancer diagnosis from
paired histopathology imaging and tabular clinical features. The proposed
methods are straightforward to implement and can be applied to any
classification task with paired image and non-image data.