# Robustly Optimized Deep Feature Decoupling Network for Fatty Liver Diseases Detection

arXiv: https://arxiv.org/abs/2406.17338 · HTML: https://mlsanity.com/paper/2406.17338/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 41 references that are in the map.
- First posted on arXiv 25 Jun 2024.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Theoretically Principled Trade-off between Robustness and Accuracy](https://mlsanity.com/paper/1901.08573/) (2019)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [Deep Long-Tailed Learning: A Survey](https://mlsanity.com/paper/2110.04596/) (2021)
- [A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises](https://mlsanity.com/paper/2008.09104/) (2020)
- [UCF: Uncovering Common Features for Generalizable Deepfake Detection](https://mlsanity.com/paper/2304.13949/) (2023)
- [Disentangled Graph Collaborative Filtering](https://mlsanity.com/paper/2007.01764/) (2020)
- [Feature Purification: How Adversarial Training Performs Robust Deep Learning](https://mlsanity.com/paper/2005.10190/) (2020)
- [Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets](https://mlsanity.com/paper/1910.08051/) (2019)
- [CFA: Class-wise Calibrated Fair Adversarial Training](https://mlsanity.com/paper/2303.14460/) (2023)
- [Analysis and Applications of Class-wise Robustness in Adversarial Training](https://mlsanity.com/paper/2105.14240/) (2021)
- [Learning by Minimizing the Sum of Ranked Range](https://mlsanity.com/paper/2010.01741/) (2020)
- [TkML-AP: Adversarial Attacks to Top-k Multi-Label Learning](https://mlsanity.com/paper/2108.00146/) (2021)
- [Rank-based Decomposable Losses in Machine Learning: A Survey](https://mlsanity.com/paper/2207.08768/) (2022)
- [Difficulty-aware Meta-learning for Rare Disease Diagnosis](https://mlsanity.com/paper/1907.00354/) (2019)
- [Sum of Ranked Range Loss for Supervised Learning](https://mlsanity.com/paper/2106.03300/) (2021)
- [Disentangled Motif-aware Graph Learning for Phrase Grounding](https://mlsanity.com/paper/2104.06008/) (2021)
- [UU-Mamba: Uncertainty-aware U-Mamba for Cardiac Image Segmentation](https://mlsanity.com/paper/2405.17496/) (2024)
- [Generalist: Decoupling Natural and Robust Generalization](https://mlsanity.com/paper/2303.13813/) (2023)
- [Robust COVID-19 Detection in CT Images with CLIP](https://mlsanity.com/paper/2403.08947/) (2024)
- [FoPro-KD: Fourier Prompted Effective Knowledge Distillation for Long-Tailed Medical Image Recognition](https://mlsanity.com/paper/2305.17421/) (2023)
- [Outlier Robust Adversarial Training](https://mlsanity.com/paper/2309.05145/) (2023)
- [Artificial Intelligence in Image-based Cardiovascular Disease Analysis](https://mlsanity.com/paper/2402.03394/) (2024)
- [Attacking Important Pixels for Anchor-free Detectors](https://mlsanity.com/paper/2301.11457/) (2023)
- [LVRNet: Lightweight Image Restoration for Aerial Images under Low Visibility](https://mlsanity.com/paper/2301.05434/) (2023)

## Built on by (most cited first, 11 of 11)

- [LLM-MedQA: Enhancing Medical Question Answering through Case Studies in Large Language Models](https://mlsanity.com/paper/2501.05464/) (2024)
- [Diffusion-empowered AutoPrompt MedSAM](https://mlsanity.com/paper/2502.06817/) (2025)
- [An Explainable Non-local Network for COVID-19 Diagnosis](https://mlsanity.com/paper/2408.04300/) (2024)
- [Robust AI-Generated Face Detection with Imbalanced Data](https://mlsanity.com/paper/2505.02182/) (2025)
- [MedChat: A Multi-Agent Framework for Multimodal Diagnosis with Large Language Models](https://mlsanity.com/paper/2506.07400/) (2025)
- [Robust Fairness Vision-Language Learning for Medical Image Analysis](https://mlsanity.com/paper/2505.03153/) (2025)
- [Non-Contrast CT Esophageal Varices Grading through Clinical Prior-Enhanced Multi-Organ Analysis](https://mlsanity.com/paper/2512.19415/) (2025)
- [Robust Multi-Source Covid-19 Detection in CT Images](https://mlsanity.com/paper/2604.03320/) (2026)
- [Weight Group-wise Post-Training Quantization for Medical Foundation Model](https://mlsanity.com/paper/2604.07674/) (2026)
- [Learning from Complementary Ultrasound Representations for Liver Disease Classification](https://mlsanity.com/paper/2607.12062/) (2026)
- [Improving Cross-Site Whole-Heart Segmentation](https://mlsanity.com/paper/2608.25109/) (2026)

## Abstract (arXiv)

Current medical image classification efforts mainly aim for higher average performance, often neglecting the balance between different classes. This can lead to significant differences in recognition accuracy between classes and obvious recognition weaknesses. Without the support of massive data, deep learning faces challenges in fine-grained classification of fatty liver. In this paper, we propose an innovative deep learning framework that combines feature decoupling and adaptive adversarial training. Firstly, we employ two iteratively compressed decouplers to supervised decouple common features and specific features related to fatty liver in abdominal ultrasound images. Subsequently, the decoupled features are concatenated with the original image after transforming the color space and are fed into the classifier. During adversarial training, we adaptively adjust the perturbation and balance the adversarial strength by the accuracy of each class. The model will eliminate recognition weaknesses by correctly classifying adversarial samples, thus improving recognition robustness. Finally, the accuracy of our method improved by 4.16%, achieving 82.95%. As demonstrated by extensive experiments, our method is a generalized learning framework that can be directly used to eliminate the recognition weaknesses of any classifier while improving its average performance. Code is available at https://github.com/HP-ML/MICCAI2024.