FaFCNN: A General Disease Classification Framework Based on Feature Fusion Neural Networks
This work addresses disease classification challenges for medical applications, but it is incremental as it builds on existing feature fusion methods.
The authors tackled the problems of insufficient training samples and poor data quality in disease classification by proposing FaFCNN, a general framework that fuses multiple source features using feature-aware interaction and alignment modules, achieving consistently optimal performance on a low-quality dataset with missing data.
There are two fundamental problems in applying deep learning/machine learning methods to disease classification tasks, one is the insufficient number and poor quality of training samples; another one is how to effectively fuse multiple source features and thus train robust classification models. To address these problems, inspired by the process of human learning knowledge, we propose the Feature-aware Fusion Correlation Neural Network (FaFCNN), which introduces a feature-aware interaction module and a feature alignment module based on domain adversarial learning. This is a general framework for disease classification, and FaFCNN improves the way existing methods obtain sample correlation features. The experimental results show that training using augmented features obtained by pre-training gradient boosting decision tree yields more performance gains than random-forest based methods. On the low-quality dataset with a large amount of missing data in our setup, FaFCNN obtains a consistently optimal performance compared to competitive baselines. In addition, extensive experiments demonstrate the robustness of the proposed method and the effectiveness of each component of the model\footnote{Accepted in IEEE SMC2023}.