  Accurate prediction of Cardiovascular disease (CVD) risk in medical imaging
is central to effective patient health management. Previous studies have
demonstrated that imaging features in computed tomography (CT) can help predict
CVD risk. However, CT entails notable radiation exposure, which may result in
adverse health effects for patients. In contrast, chest X-ray emits
significantly lower levels of radiation, offering a safer option. This
rationale motivates our investigation into the feasibility of using chest X-ray
for predicting CVD risk. Convolutional Neural Networks (CNNs) and Transformers
are two established network architectures for computer-aided diagnosis.
However, they struggle to model very high resolution chest X-ray due to the
lack of large context modeling power or quadratic time complexity. Inspired by
state space sequence models (SSMs), a new class of network architectures with
competitive sequence modeling power as Transfomers and linear time complexity,
we propose Bidirectional Image Mamba (BI-Mamba) to complement the
unidirectional SSMs with opposite directional information. BI-Mamba utilizes
parallel forward and backwark blocks to encode longe-range dependencies of
multi-view chest X-rays. We conduct extensive experiments on images from 10,395
subjects in National Lung Screening Trail (NLST). Results show that BI-Mamba
outperforms ResNet-50 and ViT-S with comparable parameter size, and saves
significant amount of GPU memory during training. Besides, BI-Mamba achieves
promising performance compared with previous state of the art in CT, unraveling
the potential of chest X-ray for CVD risk prediction.
