Data Augmentation with Multi-armed Bandit on Image Deformations Improves Fluorescence Glioma Boundary Recognition

The recognition of glioma boundary is challenging as a diffused growthing malignant tumor. Although fluorescence molecular imaging, especially in the second near-infrared window (NIR-II, 1000–1700 nm), helps improve surgical outcomes, fast and precise recognition remains in demand. Data-driven deep learning technology shows great promise in providing objective, fast, and precise recognition for glioma boundaries, but the lack of data poses challenges for designing effective models. Automatic data augmentation can improve the representation of small-scale datasets without requiring extensive prior information, which is suitable for fluorescence-based glioma boundary recognition. We propose Explore and Exploit Augment (EEA) based on multi-armed bandit for image deformations, enabling dynamic policy adjustment during training. Additionally, images captured in white light and the first near-infrared window (NIR-I, 700–900 nm) are introduced to further enhance performance. Experiments demonstrate that EEA improves the generalization of four types of models for glioma boundary recognition, suggesting significant potential for aiding in medical image classification. Code is available at https://github.com/ainieli/EEA .