Conference proceeding
PHG-Net: Persistent Homology Guided Medical Image Classification
2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp.7568-7577
01/03/2024
DOI: 10.1109/WACV57701.2024.00741
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.
Details
- Title: Subtitle
- PHG-Net: Persistent Homology Guided Medical Image Classification
- Creators
- Yaopeng Peng - University of Notre DameHongxiao Wang - University of Notre DameMilan Sonka - University of IowaDanny Z. Chen - University of Notre Dame
- Resource Type
- Conference proceeding
- Publication Details
- 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp.7568-7577
- DOI
- 10.1109/WACV57701.2024.00741
- eISSN
- 2642-9381
- Publisher
- IEEE
- Language
- English
- Date published
- 01/03/2024
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Radiation Oncology; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; Ophthalmology and Visual Sciences
- Record Identifier
- 9984622742802771
Metrics
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