PHG-Net: Persistent Homology Guided Medical Image Classification
This work addresses a domain-specific problem in medical image analysis by enhancing classification accuracy through topological feature integration, representing an incremental improvement over existing methods.
The paper tackles the problem of deep neural networks neglecting anatomical structures in medical image classification by proposing PHG-Net, which integrates topological features via persistent homology, resulting in considerable improvements over state-of-the-art methods on three public datasets.
Modern deep neural networks have achieved great successes in medical image analysis. However, the features captured by convolutional neural networks (CNNs) or Transformers tend to be optimized for pixel intensities and neglect key anatomical structures such as connected components and loops. In this paper, we propose a persistent homology guided approach (PHG-Net) that explores topological features of objects for medical image classification. For an input image, we first compute its cubical persistence diagram and extract topological features into a vector representation using a small neural network (called the PH module). The extracted topological features are then incorporated into the feature map generated by CNN or Transformer for feature fusion. The PH module is lightweight and capable of integrating topological features into any CNN or Transformer architectures in an end-to-end fashion. We evaluate our PHG-Net on three public datasets and demonstrate its considerable improvements on the target classification tasks over state-of-the-art methods.