On Multi-Modal Fusion Learning in Pathological Diagnosis of Fetal Distress

Cardiotocography (CTG) is an important medical diagnostic tool when it comes to monitoring fetal wellbeing. It records Fetal Heart Rate (FHR) and uterine contraction activity, and can be used to detect whether the fetus is receiving oxygen adequately or in distress. Unfortunately, the interpretation of CTG recordings is highly subjective which can lead to unnecessary medical intervention that represents a risk for both the mother and the fetus. In this regard, intelligent CTG (ICTG) classification is a challenging research that can assist obstetricians in making clinical decisions, thereby improving the efficiency and accuracy of pregnancy management. But, many of these models focus on one specific modality that lack generalization to unseen or test data samples. In this study, a multi-modal fusion learning approach is proposed for pathological diagnosis of fetal distress. It combines signal and image modalities for multi-modal inputs and develops a Multi-modal Encoder Network (MENet) model based on DNN for capturing the underlying distribution of multi-modal data samples. Experimental results demonstrate that under the constraints of same classifier structure, MENet performs well in terms of classification accuracy and stability, far superior to several existing ICTG algorithms.