Topo-CXR: Chest X-ray TB and Pneumonia Screening with Topological Machine Learning

Faisal Ahmed, Brighton Nuwagira, Furkan Torlak, Baris Coskunuzer; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2023, pp. 2326-2336

Examination of chest X-ray images is currently one of the most important methods for the screening and diagnosis of thoracic diseases and, in some cases, for assessing response to treatment. However, this task is time-consuming and expensive as it requires a detailed visual inspection and interpretation by a trained clinician. In the past decade, several machine learning (ML) methods have been developed to remedy this issue as clinical decision support methods. However, most of these algorithms face challenges like computational feasibility, reliability, and interpretability. In this paper, we develop a unique feature extraction method for chest X-rays by applying the latest topological data analysis (TDA) methods. We observe that normal and abnormal images produce very distinct topological patterns for pneumonia and tuberculosis. By using cubical persistence, we capture these patterns and convert them into powerful feature vectors. By combining with standard ML methods, we obtain a computationally feasible and interpretable model. In our extensive experiments, our model Topo-CXR outperforms state-of-the-art deep learning (DL) models in several benchmark datasets. Unlike most DL models, our proposed Topo-CXR model does not need any data augmentation or pre-processing steps and works perfectly on small datasets. Furthermore, our topological feature vectors can be easily integrated with any future ML and DL models to boost their performance and improve robustness.