Deep Multimodal Fusion of Data with Heterogeneous Dimensionality via Projective Networks
This addresses a limitation in medical imaging for clinicians and researchers by enabling fusion of data with different dimensions, which is incremental as it extends existing fusion methods to handle heterogeneous cases.
The paper tackles the problem of fusing multimodal data with heterogeneous dimensionality (e.g., 3D+2D) for localization tasks like segmentation, and results show it outperforms state-of-the-art monomodal methods by up to 3.10% and 4.64% Dice on specific medical imaging tasks.
The use of multimodal imaging has led to significant improvements in the diagnosis and treatment of many diseases. Similar to clinical practice, some works have demonstrated the benefits of multimodal fusion for automatic segmentation and classification using deep learning-based methods. However, current segmentation methods are limited to fusion of modalities with the same dimensionality (e.g., 3D+3D, 2D+2D), which is not always possible, and the fusion strategies implemented by classification methods are incompatible with localization tasks. In this work, we propose a novel deep learning-based framework for the fusion of multimodal data with heterogeneous dimensionality (e.g., 3D+2D) that is compatible with localization tasks. The proposed framework extracts the features of the different modalities and projects them into the common feature subspace. The projected features are then fused and further processed to obtain the final prediction. The framework was validated on the following tasks: segmentation of geographic atrophy (GA), a late-stage manifestation of age-related macular degeneration, and segmentation of retinal blood vessels (RBV) in multimodal retinal imaging. Our results show that the proposed method outperforms the state-of-the-art monomodal methods on GA and RBV segmentation by up to 3.10% and 4.64% Dice, respectively.