FOAA: Flattened Outer Arithmetic Attention For Multimodal Tumor Classification

Author:

Omnia Alwazzan, Ioannis Patras, Gregory Slabaugh

Keyword:

Computer Science, Computer Vision and Pattern Recognition, Computer Vision and Pattern Recognition (cs.CV)

journal:

date:

2024-03-10 00:00:00

Abstract

Fusion of multimodal healthcare data holds great promise to provide a holistic view of a patient's health, taking advantage of the complementarity of different modalities while leveraging their correlation. This paper proposes a simple and effective approach, inspired by attention, to fuse discriminative features from different modalities. We propose a novel attention mechanism, called Flattened Outer Arithmetic Attention (FOAA), which relies on outer arithmetic operators (addition, subtraction, product, and division) to compute attention scores from keys, queries and values derived from flattened embeddings of each modality. We demonstrate how FOAA can be implemented for self-attention and cross-attention, providing a reusable component in neural network architectures. We evaluate FOAA on two datasets for multimodal tumor classification and achieve state-of-the-art results, and we demonstrate that features enriched by FOAA are superior to those derived from other fusion approaches. The code is publicly available at \href{https://github.com/omniaalwazzan/FOAA}{https://github.com/omniaalwazzan/FOAA}

PDF: FOAA: Flattened Outer Arithmetic Attention For Multimodal Tumor Classification.pdf