  Uveitis demands the precise diagnosis of anterior chamber inflammation (ACI)
for optimal treatment. However, current diagnostic methods only rely on a
limited single-modal disease perspective, which leads to poor performance. In
this paper, we investigate a promising yet challenging way to fuse multimodal
data for ACI diagnosis. Notably, existing fusion paradigms focus on empowering
implicit modality interactions (i.e., self-attention and its variants), but
neglect to inject explicit modality interactions, especially from clinical
knowledge and imaging property. To this end, we propose a jointly Explicit and
implicit Cross-Modal Interaction Network (EiCI-Net) for Anterior Chamber
Inflammation Diagnosis that uses anterior segment optical coherence tomography
(AS-OCT) images, slit-lamp images, and clinical data jointly. Specifically, we
first develop CNN-Based Encoders and Tabular Processing Module (TPM) to extract
efficient feature representations in different modalities. Then, we devise an
Explicit Cross-Modal Interaction Module (ECIM) to generate attention maps as a
kind of explicit clinical knowledge based on the tabular feature maps, then
integrated them into the slit-lamp feature maps, allowing the CNN-Based Encoder
to focus on more effective informativeness of the slit-lamp images. After that,
the Implicit Cross-Modal Interaction Module (ICIM), a transformer-based
network, further implicitly enhances modality interactions. Finally, we
construct a considerable real-world dataset from our collaborative hospital and
conduct sufficient experiments to demonstrate the superior performance of our
proposed EiCI-Net compared with the state-of-the-art classification methods in
various metrics.
