  Medical data analysis often combines both imaging and tabular data processing
using machine learning algorithms. While previous studies have investigated the
impact of attention mechanisms on deep learning models, few have explored
integrating attention modules and tabular data. In this paper, we introduce
TabAttention, a novel module that enhances the performance of Convolutional
Neural Networks (CNNs) with an attention mechanism that is trained
conditionally on tabular data. Specifically, we extend the Convolutional Block
Attention Module to 3D by adding a Temporal Attention Module that uses
multi-head self-attention to learn attention maps. Furthermore, we enhance all
attention modules by integrating tabular data embeddings. Our approach is
demonstrated on the fetal birth weight (FBW) estimation task, using 92 fetal
abdominal ultrasound video scans and fetal biometry measurements. Our results
indicate that TabAttention outperforms clinicians and existing methods that
rely on tabular and/or imaging data for FBW prediction. This novel approach has
the potential to improve computer-aided diagnosis in various clinical workflows
where imaging and tabular data are combined. We provide a source code for
integrating TabAttention in CNNs at
https://github.com/SanoScience/Tab-Attention.
