EGDNet: an efficient glomerular detection network for multiple anomalous pathological feature in glomerulonephritis

Abstract

Glomerulonephritis (GN) is a severe kidney disorder in which the tissues in the kidney become inflamed and have problems filtering waste from the blood. Typical approaches for GN diagnosis require a specialist’s examination of pathological glomerular features (PGF) in pathology images of a patient. These PGF are primarily analyzed via manual quantitative evaluation, which is a time-consuming, labor-intensive, and error-prone task for doctors. Thus, automatic and accurate detection of PGF is crucial for the efficient diagnosis of GN and other kidney-related diseases. Recent advances in convolutional neural network-based deep learning methods have shown the capability of learning complex structural variants with promising detection results in medical image applications. However, these methods are not directly applicable to glomerular detection due to large spatial and structural variability and inter-class imbalance. Thus, we propose an efficient glomerular detection network (EGDNet) for the first time for seven types of PGF detection. Our EGDNet consists of four modules: (i) a hybrid data augmentation strategy to resolve dataset problems, (ii) an efficient intersection over unit balancing module for uniform sampling of hard and easy samples, (iii) a feature pyramid balancing module to obtain balanced multi-scale features for robust detection, and (iv) balanced L1 regression loss which alleviates the impact of anomalous data for multi-PGF detection. We also formulated a private dataset of seven PGF from an affiliated hospital in Shanghai, China. Experiments on the dataset show that our EGDNet outperforms state-of-the-art methods by achieving superior accuracy of 91.2\(\%\), 94.9\(\%\), and 94.2\(\%\) on small, medium, and large pathological features, respectively.