Fast Multi-Modal Multi-Instance Support Vector Machine for Fine-grained Chest X-ray Recognition

Chest X-ray (CXR) analysis plays an important role in patient treatment. As such, a multitude of machine learning models have been applied to CXR datasets attempting automated analysis. However, each patient has a differing number of images per angle, and multi-modal learning should deal with the missing data for specific angles and times. Furthermore, the large dimensionality of multi-modal imaging data with the shapes inconsistent across the dataset introduces the challenges in training. In light of these issues, we propose the Fast Multi-Modal Support Vector Machine (FMMSVM) which incorporates modality-specific factorization to deal with missing CXRs in the specific angle. Our model is able to adjust the fine-grained details in feature extraction and we provide an efficient optimization algorithm scalable to a large number of features. In our experiments, FMMSVM shows clearly improved classification performance.