Apr 4, 2022

Fetal Organ Anomaly Classification Network for Identifying Organ Anomalies in Fetal MRI

Frontiers in artificial intelligence
Justin Lo, Adam Lim, Matthias W Wagner, Birgit Ertl-Wagner, Dafna Sussman
Rapid development in Magnetic Resonance Imaging (MRI) has played a key role in prenatal diagnosis over the last few years. Deep learning (DL) architectures can facilitate the process of anomaly detection and affected-organ classification, making diagnosis more accurate and observer-independent. We propose a novel DL image classification architecture, Fetal Organ Anomaly Classification Network (FOAC-Net), which uses squeeze-and-excitation (SE) and naïve inception (NI) modules to automatically...