Leveraging Multimodal Fusion for Enhanced Diagnosis of Multiple Retinal
  Diseases in Ultra-wide OCTA
Hao Wei Peilun Shi Guitao Bai Minqing Zhang Shuangle Li Wu Yuan
Ultra-wide optical coherence tomography angiography (UW-OCTA) is an emerging
imaging technique that offers significant advantages over traditional OCTA by
providing an exceptionally wide scanning range of up to 24 x 20 $mm^{2}$,
covering both the anterior and posterior regions of the retina. However, the
currently accessible UW-OCTA datasets suffer from limited comprehensive
hierarchical information and corresponding disease annotations. To address this
limitation, we have curated the pioneering M3OCTA dataset, which is the first
multimodal (i.e., multilayer), multi-disease, and widest field-of-view UW-OCTA
dataset. Furthermore, the effective utilization of multi-layer ultra-wide
ocular vasculature information from UW-OCTA remains underdeveloped. To tackle
this challenge, we propose the first cross-modal fusion framework that
leverages multi-modal information for diagnosing multiple diseases. Through
extensive experiments conducted on our openly available M3OCTA dataset, we
demonstrate the effectiveness and superior performance of our method, both in
fixed and varying modalities settings. The construction of the M3OCTA dataset,
the first multimodal OCTA dataset encompassing multiple diseases, aims to
advance research in the ophthalmic image analysis community.