Early Screening of Miliary Tuberculosis with Tuberculous Meningitis Based on Few-shot Learning with Multiple Windows and Feature Granularities

Tuberculous meningitis (TBM) is a fatal tuberculosis caused by a large number of Mycobacterium tuberculosis (M. tuberculosis) spread by blood flow, with a case fatality rate of more than 50%. It is one of the most serious complications of miliary tuberculosis (MT), whose incidence is closely related to MT. If doctors can provide early diagnosis and active treatment for TBM, the case fatality rate will be significantly reduced. At present, there is a lack of methods to predict the progression of MT to TBM in clinic. To explore whether MT cases will experience TBM, we propose an early screening model of miliary tuberculosis with tuberculous meningitis (MT-TBM) based on few-shot learning with multiple windows and feature granularities (MWFG). This model aims to screen potential TBM cases through chest computerized tomography (CT) images of MT cases. Chest CT is a routine examination for MT cases. The MWFG module can extract more comprehensive features from a set of CT images of each MT case. The softmax classifier with adaptive regularization is trained on the cooperation of support set and query set, which can effectively prevent overfitting. Experiments on a dataset of 40 MT cases with chest CT images established by the medical records demonstrate that our proposed model achieves state-of-the-art performance in the early screening of MT-TBM. It can establish the connection between MT and MT-TBM through chest CT images of MT cases. The early screening model of MT-TBM based on few-shot learning with MWFG fills the research gap in computer-aided predicting TBM and has certain clinical effects. This research can provide some reference for clinicians in early diagnosis of MT-TBM and help clinicians in the early prevention and treatment of TBM for MT patients.