It is clinically important to detect liver fat content as it is related to
cardiac complications and cardiovascular disease mortality. However, existing
methods are associated with high cost and/or medical complications (e.g., liver
biopsy, medical imaging technology) or only roughly estimate the grades of
steatosis. In this paper, we propose a deep neural network to accurately
estimate liver fat percentage using only body shapes. The proposed framework is
composed of a flexible baseline regression network and a lightweight attention
module. The attention module is trained to generate discriminative and diverse
features, thus significantly improving performance. To validate our proposed
method, we perform extensive tests on medical datasets. The experimental
results validate our method and prove the efficacy of designing neural networks
to predict liver fat using only body shape. Since body shapes can be acquired
using inexpensive and readily available optical scanners, the proposed method
promised to make accurate assessment of hepatic steatosis more accessible.

使用身体形状及深度神经网络精确估计肝脏脂肪含量，并验证了这种方法的有效性，为无创检测肝脏脂肪提供了一种低成本、可行的解决方案。