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PHG-Net: Persistent Homology Guided Medical Image Classification
Preprint   Open access

PHG-Net: Persistent Homology Guided Medical Image Classification

Yaopeng Peng, Hongxiao Wang, Milan Sonka and Danny Z Chen
ArXiv.org
Cornell University
11/28/2023
DOI: 10.48550/arxiv.2311.17243
url
https://doi.org/10.48550/arxiv.2311.17243View
Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

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.
 Computer Science - Computer Vision and Pattern Recognition

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