# Liver Fat Quantification Network with Body Shape

arXiv: https://arxiv.org/abs/2405.11386 · HTML: https://mlsanity.com/paper/2405.11386/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 18 May 2024.

## Builds on (most cited first, 6 of 6)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation](https://mlsanity.com/paper/1506.06448/) (2015)
- [A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations](https://mlsanity.com/paper/1406.2639/) (2014)
- [Large-scale inference of liver fat with neural networks on UK Biobank body MRI](https://mlsanity.com/paper/2006.16777/) (2020)

## Abstract (arXiv)

It is critically important to detect the content of liver fat as it is related to cardiac complications and cardiovascular disease mortality. However, existing methods are either associated with high cost and/or medical complications (e.g., liver biopsy, imaging technology) or only roughly estimate the grades of steatosis. In this paper, we propose a deep neural network to estimate the percentage of liver fat using only body shapes. The proposed is composed of a flexible baseline network and a lightweight Attention module. The attention module is trained to generate discriminative and diverse features which significant improve the performance. In order to validate the method, we perform extensive tests on the public medical dataset. The results verify that our proposed method yields state-of-the-art performance with Root mean squared error (RMSE) of 5.26% and R-Squared value over 0.8. It offers an accurate and more accessible assessment of hepatic steatosis.